diff --git a/doc/manual/source/bibliography.rst b/doc/manual/source/bibliography.rst
index 06f4faac0816aaa2e2ea6714d54d8712aca0f555..1df9cb36de4d3438583047cc79bcc26eea16d246 100644
--- a/doc/manual/source/bibliography.rst
+++ b/doc/manual/source/bibliography.rst
@@ -47,6 +47,7 @@ Bibliography
* Hansen, Lars P. (1982): “Large sample properties of generalized method of moments estimators,” Econometrica, 50(4), 1029–1054.
* Hansen, Nikolaus and Stefan Kern (2004): “Evaluating the CMA Evolution Strategy on Multimodal Test Functions”. In: *Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII*, Proceedings, Berlin: Springer, 282–291.
* Harvey, Andrew C. and Garry D.A. Phillips (1979): “Maximum likelihood estimation of regression models with autoregressive-moving average disturbances,” *Biometrika*, 66(1), 49–58.
+* Herbst, Edward and Schorfheide, Frank (2014): "Sequential monte-carlo sampling for DSGE models," *Journal of Applied Econometrics*, 29, 1073-1098.
* Herbst, Edward (2015): “Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models,” *Computational Economics*, 45(4), 693–705.
* Ireland, Peter (2004): “A Method for Taking Models to the Data,” *Journal of Economic Dynamics and Control*, 28, 1205–26.
* Iskrev, Nikolay (2010): “Local identification in DSGE models,” *Journal of Monetary Economics*, 57(2), 189–202.
diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst
index 990fc9b22af0a91a4ce73d34837d856fc089a805..a1445ec2d11b6d7a516e63aa7953cbe0ad5ed80b 100644
--- a/doc/manual/source/the-model-file.rst
+++ b/doc/manual/source/the-model-file.rst
@@ -7454,6 +7454,18 @@ observed variables.
Chain draws than the MH-algorithm. Its relative (in)efficiency can be investigated via
the reported inefficiency factors.
+ ``'hssmc'``
+
+ Instructs Dynare to use the *Herbst and Schorfheide (2014)*
+ version of the Sequential Monte-Carlo sampler instead of the
+ standard Random-Walk Metropolis-Hastings.
+
+ ``'dsmh'``
+
+ Instructs Dynare to use the Dynamic Striated Metropolis Hastings
+ sampler proposed by *Waggoner, Wu and Zha (2016)* instead of the
+ standard Random-Walk Metropolis-Hastings.
+
.. option:: posterior_sampler_options = (NAME, VALUE, ...)
A list of NAME and VALUE pairs. Can be used to set options for
@@ -7466,141 +7478,173 @@ observed variables.
Available options are:
- .. _prop_distrib:
+ .. _prop_distrib:
- ``'proposal_distribution'``
+ ``'proposal_distribution'``
- Specifies the statistical distribution used for the
- proposal density.
+ Specifies the statistical distribution used for the
+ proposal density.
- ``'rand_multivariate_normal'``
+ ``'rand_multivariate_normal'``
- Use a multivariate normal distribution. This is the default.
+ Use a multivariate normal distribution. This is the default.
- ``'rand_multivariate_student'``
+ ``'rand_multivariate_student'``
- Use a multivariate student distribution.
+ Use a multivariate student distribution.
- ``'student_degrees_of_freedom'``
+ ``'student_degrees_of_freedom'``
- Specifies the degrees of freedom to be used with the
- multivariate student distribution. Default: ``3``.
+ Specifies the degrees of freedom to be used with the
+ multivariate student distribution. Default: ``3``.
- .. _usemhcov:
+ .. _usemhcov:
- ``'use_mh_covariance_matrix'``
+ ``'use_mh_covariance_matrix'``
- Indicates to use the covariance matrix of the draws
- from a previous MCMC run to define the covariance of
- the proposal distribution. Requires the
- :opt:`load_mh_file` option to be specified. Default:
- ``0``.
+ Indicates to use the covariance matrix of the draws
+ from a previous MCMC run to define the covariance of
+ the proposal distribution. Requires the
+ :opt:`load_mh_file` option to be specified. Default: ``0``.
- .. _scale-file:
+ .. _scale-file:
- ``'scale_file'``
+ ``'scale_file'``
- Provides the name of a ``_mh_scale.mat`` file storing
- the tuned scale factor from a previous run of
- ``mode_compute=6``.
+ Provides the name of a ``_mh_scale.mat`` file storing
+ the tuned scale factor from a previous run of
+ ``mode_compute=6``.
- .. _savetmp:
+ .. _savetmp:
- ``'save_tmp_file'``
+ ``'save_tmp_file'``
- Save the MCMC draws into a ``_mh_tmp_blck`` file at the
- refresh rate of the status bar instead of just saving
- the draws when the current ``_mh*_blck`` file is
- full. Default: ``0``
+ Save the MCMC draws into a ``_mh_tmp_blck`` file at the
+ refresh rate of the status bar instead of just saving
+ the draws when the current ``_mh*_blck`` file is
+ full. Default: ``0``
``'independent_metropolis_hastings'``
- Takes the same options as in the case of
- ``random_walk_metropolis_hastings``.
+ Takes the same options as in the case of ``random_walk_metropolis_hastings``.
``'slice'``
- ``'rotated'``
+ Available options are:
+
+ ``'rotated'``
- Triggers rotated slice iterations using a covariance
- matrix from initial burn-in iterations. Requires either
- ``use_mh_covariance_matrix`` or
- ``slice_initialize_with_mode``. Default: ``0``.
+ Triggers rotated slice iterations using a covariance
+ matrix from initial burn-in iterations. Requires either
+ ``use_mh_covariance_matrix`` or
+ ``slice_initialize_with_mode``. Default: ``0``.
- ``'mode_files'``
+ ``'mode_files'``
- For multimodal posteriors, provide the name of a file
- containing a ``nparam`` by ``nmodes`` variable called
- ``xparams`` storing the different modes. This array
- must have one column vector per mode and the estimated
- parameters along the row dimension. With this info, the
- code will automatically trigger the ``rotated`` and
- ``mode`` options. Default: ``[]``.
+ For multimodal posteriors, provide the name of a file
+ containing a ``nparam`` by ``nmodes`` variable called
+ ``xparams`` storing the different modes. This array
+ must have one column vector per mode and the estimated
+ parameters along the row dimension. With this info, the
+ code will automatically trigger the ``rotated`` and
+ ``mode`` options. Default: ``[]``.
- ``'slice_initialize_with_mode'``
+ ``'slice_initialize_with_mode'``
- The default for slice is to set ``mode_compute=0`` and
- start the chain(s) from a random location in the prior
- space. This option first runs the mode-finder and then
- starts the chain from the mode. Together with
- ``rotated``, it will use the inverse Hessian from the
- mode to perform rotated slice iterations. Default:
- ``0``.
+ The default for slice is to set ``mode_compute=0`` and
+ start the chain(s) from a random location in the prior
+ space. This option first runs the mode-finder and then
+ starts the chain from the mode. Together with
+ ``rotated``, it will use the inverse Hessian from the
+ mode to perform rotated slice iterations. Default:
+ ``0``.
- ``'initial_step_size'``
+ ``'initial_step_size'``
- Sets the initial size of the interval in the
- stepping-out procedure as fraction of the prior
- support, i.e. the size will be ``initial_step_size *
- (UB-LB)``. ``initial_step_size`` must be a real number
- in the interval ``[0,1]``. Default: ``0.8``.
+ Sets the initial size of the interval in the
+ stepping-out procedure as fraction of the prior
+ support, i.e. the size will be ``initial_step_size *
+ (UB-LB)``. ``initial_step_size`` must be a real number
+ in the interval ``[0,1]``. Default: ``0.8``.
- ``'use_mh_covariance_matrix'``
+ ``'use_mh_covariance_matrix'``
- See :ref:`use_mh_covariance_matrix <usemhcov>`. Must be
- used with ``'rotated'``. Default: ``0``.
+ See :ref:`use_mh_covariance_matrix <usemhcov>`. Must be
+ used with ``'rotated'``. Default: ``0``.
- ``'save_tmp_file'``
+ ``'save_tmp_file'``
- See :ref:`save_tmp_file <savetmp>`. Default: ``1``.
+ See :ref:`save_tmp_file <savetmp>`. Default: ``1``.
``'tailored_random_block_metropolis_hastings'``
- ``'proposal_distribution'``
+ Available options are:
+
+ ``'proposal_distribution'``
+
+ Specifies the statistical distribution used for the
+ proposal density. See :ref:`proposal_distribution <prop_distrib>`.
+
+ ``new_block_probability = DOUBLE``
+
+ Specifies the probability of the next parameter
+ belonging to a new block when the random blocking in
+ the TaRB Metropolis-Hastings algorithm is
+ conducted. The higher this number, the smaller is the
+ average block size and the more random blocks are
+ formed during each parameter sweep. Default: ``0.25``.
+
+ ``mode_compute = INTEGER``
+
+ Specifies the mode-finder run in every iteration for
+ every block of the TaRB Metropolis-Hastings
+ algorithm. See :opt:`mode_compute <mode_compute =
+ INTEGER | FUNCTION_NAME>`. Default: ``4``.
+
+ ``optim = (NAME, VALUE,...)``
+
+ Specifies the options for the mode-finder used in the
+ TaRB Metropolis-Hastings algorithm. See :opt:`optim
+ <optim = (NAME, VALUE, ...)>`.
+
+ ``'scale_file'``
+
+ See :ref:`scale_file <scale-file>`..
+
+ ``'save_tmp_file'``
+
+ See :ref:`save_tmp_file <savetmp>`. Default: ``1``.
+
+ ``'hssmc'``
+
+ Available options are:
+
+ ``'particles'``
- Specifies the statistical distribution used for the
- proposal density. See :ref:`proposal_distribution <prop_distrib>`.
+ Number of particles. Default value is: 20000.
- ``new_block_probability = DOUBLE``
+ ``'steps'``
- Specifies the probability of the next parameter
- belonging to a new block when the random blocking in
- the TaRB Metropolis-Hastings algorithm is
- conducted. The higher this number, the smaller is the
- average block size and the more random blocks are
- formed during each parameter sweep. Default: ``0.25``.
+ Number of weights :math:`\phi_i\in[0,1]` on the likelihood function used to define a sequence of tempered likelihoods. This parameter is denoted :math:`N_{\phi}` in *Herbst and Schorfheide (2014)*, and we have :math:`\phi_1=0` and :math:`\phi_{N_\phi}=1`. Default value is: 25.
- ``mode_compute = INTEGER``
+ ``'lambda'``
- Specifies the mode-finder run in every iteration for
- every block of the TaRB Metropolis-Hastings
- algorithm. See :opt:`mode_compute <mode_compute =
- INTEGER | FUNCTION_NAME>`. Default: ``4``.
+ Positive parameter controling the sequence of weights :math:`\phi_i`, Default value is: 2. Weights are defined by:
- ``optim = (NAME, VALUE,...)``
+ .. math::
- Specifies the options for the mode-finder used in the
- TaRB Metropolis-Hastings algorithm. See :opt:`optim
- <optim = (NAME, VALUE, ...)>`.
+ \phi_i = \left(\frac{i-1}{N_{\phi}-1}\right)^{\lambda}
- ``'scale_file'``
+ for :math:`i=1,\ldots,N_{\phi}`. Usually we set :math:`\lambda>1`, so that :math:`\Delta \phi_i = \phi_i-\phi_{i-1}` is increasing with :math:`i`.
- See :ref:`scale_file <scale-file>`..
+ ``'target'``
- ``'save_tmp_file'``
+ Acceptance rate target. Default value is: .25.
- See :ref:`save_tmp_file <savetmp>`. Default: ``1``.
+ ``'scale'``
+ Scale parameter in the mutation step (on the proposal covariance matrix of the MH iteration). Default value is: .5.
+
.. option:: moments_varendo
Triggers the computation of the posterior distribution of the
diff --git a/matlab/@dprior/admissible.m b/matlab/@dprior/admissible.m
new file mode 100644
index 0000000000000000000000000000000000000000..c3a46c36b530869d61968a2e878a60382dbcc1c2
--- /dev/null
+++ b/matlab/@dprior/admissible.m
@@ -0,0 +1,146 @@
+function b = admissible(o, d)
+
+% Return true iff d is an admissible draw in a distribution characterized by o.
+%
+% INPUTS
+% - o [dprior] Distribution specification for a n×1 vector of independent continuous random variables
+% - d [double] n×1 vector.
+%
+% OUTPUTS
+% - b [logical] scalar.
+%
+% REMARKS
+% None.
+%
+% EXAMPLE
+%
+% >> Prior = dprior(bayestopt_, options_.prior_trunc);
+% >> d = Prior.draw()
+% >> Prior.admissible(d)
+% ans =
+%
+% logical
+%
+% 1
+
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+b = false;
+
+if ~isequal(length(d), length(o.lb))
+ return
+end
+
+if all(d>=o.lb & d<=o.ub)
+ b = true;
+end
+
+return % --*-- Unit tests --*--
+
+%@test:1
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
+p1 = .4*ones(14,1); % Prior mean
+p2 = .2*ones(14,1); % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+ switch p0(i)
+ case 1
+ % Beta distribution
+ p3(i) = 0;
+ p4(i) = 1;
+ [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+ p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+ case 2
+ % Gamma distribution
+ p3(i) = 0;
+ p4(i) = Inf;
+ [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+ p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+ case 3
+ % Normal distribution
+ p3(i) = -Inf;
+ p4(i) = Inf;
+ p6(i) = p1(i);
+ p7(i) = p2(i);
+ p5(i) = p1(i);
+ case 4
+ % Inverse Gamma (type I) distribution
+ p3(i) = 0;
+ p4(i) = Inf;
+ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+ p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+ case 5
+ % Uniform distribution
+ [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+ p3(i) = p6(i);
+ p4(i) = p7(i);
+ p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+ case 6
+ % Inverse Gamma (type II) distribution
+ p3(i) = 0;
+ p4(i) = Inf;
+ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+ p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+ case 8
+ % Weibull distribution
+ p3(i) = 0;
+ p4(i) = Inf;
+ [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+ p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+ otherwise
+ error('This density is not implemented!')
+ end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+ndraws = 10;
+
+% Call the tested routine
+try
+ % Instantiate dprior object
+ o = dprior(BayesInfo, prior_trunc, false);
+ % Do simulations in a loop and estimate recursively the mean and the variance.
+ for i = 1:ndraws
+ draw = o.draw();
+ if ~o.admissible(draw)
+ error()
+ end
+ end
+ t(1) = true;
+catch
+ t(1) = false;
+end
+
+T = all(t);
+
+%@eof:1
diff --git a/matlab/tempered_likelihood.m b/matlab/@dprior/length.m
similarity index 50%
rename from matlab/tempered_likelihood.m
rename to matlab/@dprior/length.m
index 34df1f64f0e66560a3dba0f2a016ad32a1ecc42b..6e25d6360aeb7bb8721371a81ee127921d701115 100644
--- a/matlab/tempered_likelihood.m
+++ b/matlab/@dprior/length.m
@@ -1,7 +1,17 @@
-function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_)
-% function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_)
+function n = length(o)
-% Copyright © 2022 Dynare Team
+% Return the dimension of the random vector.
+%
+% INPUTS
+% - o [dprior] Distribution specification for a n×1 vector of independent continuous random variables
+%
+% OUTPUTS
+% - n [integer] scalar.
+%
+% REMARKS
+% None.
+
+% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -18,7 +28,4 @@ function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,da
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
- logpostkern = -feval(TargetFun,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
- logprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
- loglik = logpostkern-logprior ;
- tlogpostkern = lambda*loglik + logprior;
+n = length(o.lb);
diff --git a/matlab/@dprior/subsref.m b/matlab/@dprior/subsref.m
index 4e6cf3ea16edb7334bf25de7cdfc84f260d4745f..faea66d53ba0399125922eab70b73132368dd9c5 100644
--- a/matlab/@dprior/subsref.m
+++ b/matlab/@dprior/subsref.m
@@ -23,9 +23,9 @@ switch S(1).type
case '.'
if ismember(S(1).subs, {'p1','p2','p3','p4','p5','p6','p7','lb','ub'})
p = builtin('subsref', o, S(1));
- elseif ismember(S(1).subs, {'draw'})
+ elseif ismember(S(1).subs, {'draw','length'})
p = feval(S(1).subs, o);
- elseif ismember(S(1).subs, {'draws', 'density', 'densities', 'moments'})
+ elseif ismember(S(1).subs, {'draws', 'density', 'densities', 'moments', 'admissible'})
p = feval(S(1).subs, o , S(2).subs{:});
elseif ismember(S(1).subs, {'mean', 'median', 'variance', 'mode'})
if (length(S)==2 && isempty(S(2).subs)) || length(S)==1
diff --git a/matlab/GetAllPosteriorDraws.m b/matlab/GetAllPosteriorDraws.m
index 23222b40d7953d1e8084bd3f019be18f132dfa87..dcb6e80dc7eb25919b2388bc5c3581dc2988f915 100644
--- a/matlab/GetAllPosteriorDraws.m
+++ b/matlab/GetAllPosteriorDraws.m
@@ -1,23 +1,24 @@
-function Draws = GetAllPosteriorDraws(dname, fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, nblcks, blck)
-% function Draws = GetAllPosteriorDraws(dname, fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, nblcks, blck)
-% Gets all posterior draws
+function draws = GetAllPosteriorDraws(options_, dname, fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, nblcks, blck)
+
+% Gets all posterior draws.
%
% INPUTS
-% dname: name of directory with results
-% fname: name of mod file
-% column: column of desired parameter in draw matrix
-% FirstMhFile: first mh file
-% FirstLine: first line
-% TotalNumberOfMhFile: total number of mh file
-% NumberOfDraws: number of draws
-% nblcks: total number of blocks
-% blck: desired block to read
+% - options_ [struct] Dynare's options.
+% - dname [char] name of directory with results.
+% - fname [char] name of mod file.
+% - column [integer] scalar, parameter index.
+% - FirstMhFile [integer] scalar, first MH file.
+% - FirstLine [integer] scalar, first line in first MH file.
+% - TotalNumberOfMhFile [integer] scalar, total number of MH file.
+% - NumberOfDraws [integer] scalar, number of posterior draws.
+% - nblcks [integer] scalar, total number of blocks.
+% - blck: [integer] scalar, desired block to read.
%
% OUTPUTS
-% Draws: draws from posterior distribution
+% - draws: [double] NumberOfDraws×1 vector, draws from posterior distribution.
%
-% SPECIAL REQUIREMENTS
-% none
+% REMARKS
+% Only the first and third input arguments are required for SMC samplers.
% Copyright © 2005-2023 Dynare Team
%
@@ -36,55 +37,61 @@ function Draws = GetAllPosteriorDraws(dname, fname, column, FirstMhFile, FirstLi
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-iline = FirstLine;
-linee = 1;
-DirectoryName = CheckPath('metropolis',dname);
-
-if nblcks>1 && nargin<9
- Draws = zeros(NumberOfDraws*nblcks,1);
- iline0=iline;
- if column>0
- for blck = 1:nblcks
- iline=iline0;
+if ishssmc(options_)
+ % Load draws from the posterior distribution
+ pfiles = dir(sprintf('%s/hssmc/particles-*.mat', dname));
+ posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', dname, length(pfiles), length(pfiles)));
+ draws = transpose(posterior.particles(column,:));
+else
+ iline = FirstLine;
+ linee = 1;
+ DirectoryName = CheckPath('metropolis',dname);
+ if nblcks>1 && nargin<10
+ draws = zeros(NumberOfDraws*nblcks,1);
+ iline0=iline;
+ if column>0
+ for blck = 1:nblcks
+ iline=iline0;
+ for file = FirstMhFile:TotalNumberOfMhFile
+ load([DirectoryName '/' fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
+ NumberOfLines = size(x2(iline:end,:),1);
+ draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
+ linee = linee+NumberOfLines;
+ iline = 1;
+ end
+ end
+ else
+ for blck = 1:nblcks
+ iline=iline0;
+ for file = FirstMhFile:TotalNumberOfMhFile
+ load([DirectoryName '/' fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
+ NumberOfLines = size(logpo2(iline:end),1);
+ draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
+ linee = linee+NumberOfLines;
+ iline = 1;
+ end
+ end
+ end
+ else
+ if nblcks==1
+ blck=1;
+ end
+ if column>0
for file = FirstMhFile:TotalNumberOfMhFile
load([DirectoryName '/' fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
NumberOfLines = size(x2(iline:end,:),1);
- Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
+ draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
linee = linee+NumberOfLines;
iline = 1;
end
- end
- else
- for blck = 1:nblcks
- iline=iline0;
+ else
for file = FirstMhFile:TotalNumberOfMhFile
load([DirectoryName '/' fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
- NumberOfLines = size(logpo2(iline:end),1);
- Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
+ NumberOfLines = size(logpo2(iline:end,:),1);
+ draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
linee = linee+NumberOfLines;
iline = 1;
end
end
end
-else
- if nblcks==1
- blck=1;
- end
- if column>0
- for file = FirstMhFile:TotalNumberOfMhFile
- load([DirectoryName '/' fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
- NumberOfLines = size(x2(iline:end,:),1);
- Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
- linee = linee+NumberOfLines;
- iline = 1;
- end
- else
- for file = FirstMhFile:TotalNumberOfMhFile
- load([DirectoryName '/' fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
- NumberOfLines = size(logpo2(iline:end,:),1);
- Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
- linee = linee+NumberOfLines;
- iline = 1;
- end
- end
-end
\ No newline at end of file
+end
diff --git a/matlab/GetPosteriorMeanVariance.m b/matlab/GetPosteriorMeanVariance.m
index 5a2c44af2b2a79245af0a6f995a08049a2ba2b08..37ef07411e6757f4e2b2ecc0363e879b096500e6 100644
--- a/matlab/GetPosteriorMeanVariance.m
+++ b/matlab/GetPosteriorMeanVariance.m
@@ -1,18 +1,15 @@
-function [mean,variance] = GetPosteriorMeanVariance(M_,drop)
+function [mean, variance] = GetPosteriorMeanVariance(options_, M_)
%function [mean,variance] = GetPosteriorMeanVariance(M,drop)
% Computes the posterior mean and variance
% (+updates of oo_ & TeX output).
%
% INPUTS
-% M_ [structure] Dynare model structure
-% drop [double] share of draws to drop
+% - options_ [struct] Dynare's options.
+% - M_ [struct] Description of the model.
%
% OUTPUTS
-% mean [double] mean parameter vector
-% variance [double] variance
-%
-% SPECIAL REQUIREMENTS
-% None.
+% - mean [double] n×1 vector, posterior expectation.
+% - variance [double] n×n matrix, posterior variance.
% Copyright © 2012-2023 Dynare Team
%
@@ -31,37 +28,46 @@ function [mean,variance] = GetPosteriorMeanVariance(M_,drop)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-MetropolisFolder = CheckPath('metropolis',M_.dname);
-FileName = M_.fname;
-BaseName = [MetropolisFolder filesep FileName];
-record=load_last_mh_history_file(MetropolisFolder, FileName);
-NbrDraws = sum(record.MhDraws(:,1));
-NbrFiles = sum(record.MhDraws(:,2));
-NbrBlocks = record.Nblck;
-mean = 0;
-variance = 0;
-
-NbrKeptDraws = 0;
-for i=1:NbrBlocks
- NbrDrawsCurrentBlock = 0;
- for j=1:NbrFiles
- o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
- NbrDrawsCurrentFile = size(o.x2,1);
- if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= drop*NbrDraws
+if ishssmc(options_)
+ % Load draws from the posterior distribution
+ pfiles = dir(sprintf('%s/hssmc/particles-*.mat', M_.dname));
+ posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', M_.dname, length(pfiles), length(pfiles)));
+ % Compute the posterior mean
+ mean = sum(posterior.particles, 2)/length(posterior.tlogpostkernel);
+ % Compute the posterior covariance
+ variance = (posterior.particles-mean)*(posterior.particles-mean)'/length(posterior.tlogpostkernel);
+else
+ MetropolisFolder = CheckPath('metropolis',M_.dname);
+ FileName = M_.fname;
+ BaseName = [MetropolisFolder filesep FileName];
+ record=load_last_mh_history_file(MetropolisFolder, FileName);
+ NbrDraws = sum(record.MhDraws(:,1));
+ NbrFiles = sum(record.MhDraws(:,2));
+ NbrBlocks = record.Nblck;
+ mean = 0;
+ variance = 0;
+ NbrKeptDraws = 0;
+ for i=1:NbrBlocks
+ NbrDrawsCurrentBlock = 0;
+ for j=1:NbrFiles
+ o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
+ NbrDrawsCurrentFile = size(o.x2,1);
+ if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= options_.mh_drop*NbrDraws
+ NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
+ continue
+ elseif NbrDrawsCurrentBlock < options_.mh_drop*NbrDraws
+ FirstDraw = ceil(options_.mh_drop*NbrDraws - NbrDrawsCurrentBlock + 1);
+ x2 = o.x2(FirstDraw:end,:);
+ else
+ x2 = o.x2;
+ end
+ NbrKeptDrawsCurrentFile = size(x2,1);
+ %recursively compute mean and variance
+ mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+ x2Demeaned = bsxfun(@minus,x2,mean');
+ variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
- continue
- elseif NbrDrawsCurrentBlock < drop*NbrDraws
- FirstDraw = ceil(drop*NbrDraws - NbrDrawsCurrentBlock + 1);
- x2 = o.x2(FirstDraw:end,:);
- else
- x2 = o.x2;
+ NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
end
- NbrKeptDrawsCurrentFile = size(x2,1);
- %recursively compute mean and variance
- mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
- x2Demeaned = bsxfun(@minus,x2,mean');
- variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
- NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
- NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
end
end
diff --git a/matlab/GetPosteriorParametersStatistics.m b/matlab/GetPosteriorParametersStatistics.m
index bd29b1f61a464dbc3b34e2572801d5608f5accf5..e54964abd8e158b687959b38598b1891e1304367 100644
--- a/matlab/GetPosteriorParametersStatistics.m
+++ b/matlab/GetPosteriorParametersStatistics.m
@@ -1,5 +1,5 @@
function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames)
-% function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames)
+
% This function prints and saves posterior estimates after the mcmc
% (+updates of oo_ & TeX output).
%
@@ -34,10 +34,6 @@ function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bay
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-%if ~options_.mh_replic && options_.load_mh_file
-% load([M_.fname '_results.mat'],'oo_');
-%end
-
TeX = options_.TeX;
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
@@ -45,19 +41,20 @@ ncx = estim_params_.ncx;
ncn = estim_params_.ncn;
np = estim_params_.np ;
-MetropolisFolder = CheckPath('metropolis',M_.dname);
latexFolder = CheckPath('latex',M_.dname);
FileName = M_.fname;
-record=load_last_mh_history_file(MetropolisFolder,FileName);
-
-FirstLine = record.KeepedDraws.FirstLine;
-TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
-TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
-FirstMhFile = record.KeepedDraws.FirstMhFile;
-NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
-mh_nblck = size(record.LastParameters,1);
-clear record;
+if ~issmc(options_)
+ MetropolisFolder = CheckPath('metropolis',M_.dname);
+ record=load_last_mh_history_file(MetropolisFolder,FileName);
+ FirstLine = record.KeepedDraws.FirstLine;
+ TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
+ TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
+ FirstMhFile = record.KeepedDraws.FirstMhFile;
+ NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
+ mh_nblck = size(record.LastParameters,1);
+ clear record;
+end
header_width = row_header_width(M_, estim_params_, bayestopt_);
hpd_interval=[num2str(options_.mh_conf_sig*100), '% HPD interval'];
@@ -68,13 +65,26 @@ skipline(2)
disp('ESTIMATION RESULTS')
skipline()
-if ~isfield(oo_,'MarginalDensity') || ~isfield(oo_.MarginalDensity,'ModifiedHarmonicMean')
- [~,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+if ishssmc(options_)
+ dprintf('Log data density is %f.', oo_.MarginalDensity.hssmc);
+ % Set function handle for GetAllPosteriorDraws
+ getalldraws = @(i) GetAllPosteriorDraws(options_, M_.dname, [], i);
+else
+ if ~isfield(oo_,'MarginalDensity') || (issmc(options_) && ~isfield(oo_.MarginalDensity,'ModifiedHarmonicMean'))
+ [~, oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+ end
+ fprintf('Log data density is %f.', oo_.MarginalDensity.ModifiedHarmonicMean);
+ % Set function handle for GetAllPosteriordraws
+ getalldraws = @(i) GetAllPosteriorDraws(options_, M_.dname, M_.fname, i, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
end
-fprintf('\nLog data density is %f.\n', oo_.MarginalDensity.ModifiedHarmonicMean);
-num_draws=NumberOfDraws*mh_nblck;
-hpd_draws = round((1-options_.mh_conf_sig)*num_draws);
+if ishssmc(options_)
+ num_draws = options_.posterior_sampler_options.hssmc.particles;
+ hpd_draws = round((1-options_.mh_conf_sig)*num_draws);
+else
+ num_draws=NumberOfDraws*mh_nblck;
+ hpd_draws = round((1-options_.mh_conf_sig)*num_draws);
+end
if hpd_draws<2
fprintf('posterior_moments: There are not enough draws computes to compute HPD Intervals. Skipping their computation.\n')
@@ -93,9 +103,9 @@ if np
disp(tit2)
ip = nvx+nvn+ncx+ncn+1;
for i=1:np
- if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh) || ishssmc(options_)
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
name = bayestopt_.name{ip};
oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
else
@@ -103,8 +113,8 @@ if np
name = bayestopt_.name{ip};
[post_mean, hpd_interval, post_var] = Extractoo(oo_, name, type);
catch
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
name = bayestopt_.name{ip};
oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
end
@@ -137,8 +147,8 @@ if nvx
disp(tit2)
for i=1:nvx
if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
k = estim_params_.var_exo(i,1);
name = M_.exo_names{k};
oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
@@ -149,9 +159,8 @@ if nvx
name = M_.exo_names{k};
[post_mean, hpd_interval, post_var] = Extractoo(oo_, name, type);
catch
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
- posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
k = estim_params_.var_exo(i,1);
name = M_.exo_names{k};
oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
@@ -181,8 +190,8 @@ if nvn
ip = nvx+1;
for i=1:nvn
if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
else
@@ -190,8 +199,8 @@ if nvn
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
catch
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws,1,options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws,1,options_.mh_conf_sig);
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
end
@@ -220,8 +229,8 @@ if ncx
ip = nvx+nvn+1;
for i=1:ncx
if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws,1,options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws,1,options_.mh_conf_sig);
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name = sprintf('%s,%s', M_.exo_names{k1}, M_.exo_names{k2});
@@ -237,8 +246,8 @@ if ncx
NAME = sprintf('%s_%s', M_.exo_names{k1}, M_.exo_names{k2});
[post_mean,hpd_interval,post_var] = Extractoo(oo_, NAME, type);
catch
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name = sprintf('%s,%s', M_.exo_names{k1}, M_.exo_names{k2});
@@ -259,6 +268,7 @@ if ncx
TeXEnd(fid, 4, 'correlation of structural shocks');
end
end
+
if ncn
type = 'measurement_errors_corr';
if TeX
@@ -270,8 +280,8 @@ if ncn
ip = nvx+nvn+ncx+1;
for i=1:ncn
if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
name = sprintf('%s,%s', M_.endo_names{k1}, M_.endo_names{k2});
@@ -285,8 +295,8 @@ if ncn
NAME = sprintf('%s_%s', M_.endo_names{k1}, M_.endo_names{k2});
[post_mean,hpd_interval,post_var] = Extractoo(oo_, NAME, type);
catch
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
- [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+ draws = getalldraws(ip);
+ [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
name = sprintf('%s,%s', M_.endo_names{k1}, M_.endo_names{k2});
@@ -333,11 +343,8 @@ fprintf(fidTeX, ' & \\multicolumn{3}{c}{Prior} & \\multicolumn{4}{c}{Posterio
fprintf(fidTeX, ' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-8}\n');
fprintf(fidTeX, ' & Dist. & Mean & Stdev. & Mean & Stdev. & HPD inf & HPD sup\\\\\n');
fprintf(fidTeX, '\\midrule \\endhead \n');
-
fprintf(fidTeX, '\\bottomrule \\multicolumn{8}{r}{(Continued on next page)} \\endfoot \n');
fprintf(fidTeX, '\\bottomrule \\endlastfoot \n');
-
-
fid = fidTeX;
@@ -375,4 +382,4 @@ hpd_interval = zeros(2,1);
post_mean = oo.posterior_mean.(type).(name);
hpd_interval(1) = oo.posterior_hpdinf.(type).(name);
hpd_interval(2) = oo.posterior_hpdsup.(type).(name);
-post_var = oo.posterior_variance.(type).(name);
\ No newline at end of file
+post_var = oo.posterior_variance.(type).(name);
diff --git a/matlab/Herbst_Schorfheide_sampler.m b/matlab/Herbst_Schorfheide_sampler.m
deleted file mode 100644
index f6881bc840b2081ed7902b1484eb702edd6744ca..0000000000000000000000000000000000000000
--- a/matlab/Herbst_Schorfheide_sampler.m
+++ /dev/null
@@ -1,246 +0,0 @@
-function Herbst_Schorfheide_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% function Herbst_Schorfheide_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% SMC sampler from JAE 2014 .
-%
-% INPUTS
-% o TargetFun [char] string specifying the name of the objective
-% function (posterior kernel).
-% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
-% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
-% o dataset_ data structure
-% o dataset_info dataset info structure
-% o options_ options structure
-% o M_ model structure
-% o estim_params_ estimated parameters structure
-% o bayestopt_ estimation options structure
-% o oo_ outputs structure
-%
-% SPECIAL REQUIREMENTS
-% None.
-%
-% PARALLEL CONTEXT
-% The most computationally intensive part of this function may be executed
-% in parallel. The code suitable to be executed in
-% parallel on multi core or cluster machine (in general a 'for' cycle)
-% has been removed from this function and been placed in the posterior_sampler_core.m funtion.
-%
-% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
-% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
-% to manage the parallel computations.
-%
-% This function was the first function to be parallelized. Later, other
-% functions have been parallelized using the same methodology.
-% Then the comments write here can be used for all the other pairs of
-% parallel functions and also for management functions.
-
-% Copyright © 2006-2023 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-
-
-% Create the tempering schedule
-phi = bsxfun(@power,(bsxfun(@minus,1:1:options_.posterior_sampler_options.HSsmc.nphi,1)/(options_.posterior_sampler_options.HSsmc.nphi-1)),options_.posterior_sampler_options.HSsmc.lambda) ;
-% tuning for MH algorithms matrices
-zhat = 0 ; % normalization constant
-csim = zeros(options_.posterior_sampler_options.HSsmc.nphi,1) ; % scale parameter
-ESSsim = zeros(options_.posterior_sampler_options.HSsmc.nphi,1) ; % ESS
-acptsim = zeros(options_.posterior_sampler_options.HSsmc.nphi,1) ; % average acceptance rate
-% Step 0: Initialization of the sampler
-[ param, tlogpost_i, loglik, bayestopt_] = ...
- SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,options_.posterior_sampler_options.HSsmc.nparticles);
-weights = ones(options_.posterior_sampler_options.HSsmc.nparticles,1)/options_.posterior_sampler_options.HSsmc.nparticles ;
-% The Herbst and Schorfheide sampler starts here
-for i=2:options_.posterior_sampler_options.HSsmc.nphi
- % (a) Correction
- % incremental weights
- incwt = exp((phi(i)-phi(i-1))*loglik) ;
- % update weights
- weights = bsxfun(@times,weights,incwt) ;
- sum_weights = sum(weights) ;
- zhat = zhat + log(sum_weights) ;
- % normalize weights
- weights = weights/sum_weights ;
- % (b) Selection
- ESSsim(i) = 1/sum(weights.^2) ;
- if (ESSsim(i) < options_.posterior_sampler_options.HSsmc.nparticles/2)
- indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.HSsmc.nparticles) ;
- param = param(:,indx_resmpl) ;
- loglik = loglik(indx_resmpl) ;
- tlogpost_i = tlogpost_i(indx_resmpl) ;
- weights = ones(options_.posterior_sampler_options.HSsmc.nparticles,1)/options_.posterior_sampler_options.HSsmc.nparticles ;
- end
- % (c) Mutation
- options_.posterior_sampler_options.HSsmc.c = options_.posterior_sampler_options.HSsmc.c*modified_logit(0.95,0.1,16.0,options_.posterior_sampler_options.HSsmc.acpt-options_.posterior_sampler_options.HSsmc.trgt) ;
- % Calculate estimates of mean and variance
- mu = param*weights ;
- z = bsxfun(@minus,param,mu) ;
- R = z*(bsxfun(@times,z',weights)) ;
- Rchol = chol(R)' ;
- % Mutation
- if options_.posterior_sampler_options.HSsmc.option_mutation==1
- [param,tlogpost_i,loglik,options_.posterior_sampler_options.HSsmc.acpt] = mutation_RW(TargetFun,param,tlogpost_i,loglik,phi,i,options_.posterior_sampler_options.HSsmc.c*Rchol,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_) ;
- elseif options_.posterior_sampler_options.HSsmc.option_mutation==2
- inv_R = inv(options_.posterior_sampler_options.HSsmc.c^2*R) ;
- Rdiagchol = sqrt(diag(R)) ;
- [param,tlogpost_i,loglik,options_.posterior_sampler_options.HSsmc.acpt] = mutation_Mixture(TargetFun,param,tlogpost_i,loglik,phi,i,options_.posterior_sampler_options.HSsmc.c*Rchol,options_.posterior_sampler_options.HSsmc.c*Rdiagchol,inv_R,mu,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_) ;
- end
- acptsim(i) = options_.posterior_sampler_options.HSsmc.acpt ;
- csim(i) = options_.posterior_sampler_options.HSsmc.c ;
- % print information
- fprintf(' Iteration = %5.0f / %5.0f \n', i, options_.posterior_sampler_options.HSsmc.nphi);
- fprintf(' phi = %5.4f \n', phi(i));
- fprintf(' Neff = %5.4f \n', ESSsim(i));
- fprintf(' %accept. = %5.4f \n', acptsim(i));
-end
-indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.HSsmc.nparticles);
-distrib_param = param(:,indx_resmpl);
-fprintf(' Log_lik = %5.4f \n', zhat);
-
-mean_xparam = mean(distrib_param,2);
-npar = length(xparam1) ;
-%mat_var_cov = bsxfun(@minus,distrib_param,mean_xparam) ;
-%mat_var_cov = (mat_var_cov*mat_var_cov')/(options_.posterior_sampler_options.HSsmc.nparticles-1) ;
-%std_xparam = sqrt(diag(mat_var_cov)) ;
-lb95_xparam = zeros(npar,1) ;
-ub95_xparam = zeros(npar,1) ;
-for i=1:npar
- temp = sortrows(distrib_param(i,:)') ;
- lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.HSsmc.nparticles) ;
- ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.HSsmc.nparticles) ;
-end
-
-TeX = options_.TeX;
-
-str = sprintf(' Param. \t Lower Bound (95%%) \t Mean \t Upper Bound (95%%)');
-for l=1:npar
- [name,~] = get_the_name(l,TeX,M_,estim_params_,options_.varobs);
- str = sprintf('%s\n %s \t\t %5.4f \t\t %7.5f \t\t %5.4f', str, name, lb95_xparam(l), mean_xparam(l), ub95_xparam(l));
-end
-disp([str])
-disp('')
-
-%% Plot parameters densities
-
-[nbplt,nr,nc,lr,lc,nstar] = pltorg(npar);
-
-if TeX
- fidTeX = fopen([M_.fname '_param_density.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by DSMH.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
-end
-
-number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
-bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
-kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
-plt = 1 ;
-%for plt = 1:nbplt,
-if TeX
- NAMES = [];
- TeXNAMES = [];
-end
-hh_fig = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
-for k=1:npar %min(nstar,npar-(plt-1)*nstar)
- subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
- %kk = (plt-1)*nstar+k;
- [name,texname] = get_the_name(k,TeX,M_,estim_params_,options_.varobs);
- optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.HSsmc.nparticles,bandwidth,kernel_function);
- [density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
- options_.posterior_sampler_options.HSsmc.nparticles,optimal_bandwidth,kernel_function);
- plot(density(:,1),density(:,2));
- hold on
- if TeX
- title(texname,'interpreter','latex')
- else
- title(name,'interpreter','none')
- end
- hold off
- axis tight
- drawnow
-end
-dyn_saveas(hh_fig,[ M_.fname '_param_density' int2str(plt) ],options_.nodisplay,options_.graph_format);
-if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- % TeX eps loader file
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%_param_density%s}\n',min(k/floor(sqrt(npar)),1),M_.fname,int2str(plt));
- fprintf(fidTeX,'\\caption{Parameter densities based on the Herbst/Schorfheide sampler.}');
- fprintf(fidTeX,'\\label{Fig:ParametersDensities:%s}\n',int2str(plt));
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
-end
-%end
-
-function [param,tlogpost_i,loglik,acpt] = mutation_RW(TargetFun,param,tlogpost_i,loglik,phi,i,cRchol,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-acpt = 0.0 ;
-tlogpost_i = tlogpost_i + (phi(i)-phi(i-1))*loglik ;
-for j=1:options_.posterior_sampler_options.HSsmc.nparticles
- validate= 0;
- while validate==0
- candidate = param(:,j) + cRchol*randn(size(param,1),1) ;
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
- [tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,phi(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
- validate = 1;
- if rand(1,1)<exp(tlogpostx-tlogpost_i(j)) % accept
- acpt = acpt + 1 ;
- param(:,j) = candidate;
- loglik(j) = loglikx;
- tlogpost_i(j) = tlogpostx;
- end
- end
- end
- end
-end
-acpt = acpt/options_.posterior_sampler_options.HSsmc.nparticles;
-
-function [param,tlogpost_i,loglik,acpt] = mutation_Mixture(TargetFun,param,tlogpost_i,loglik,phi,i,cRchol,cRdiagchol,invR,mu,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-acpt = 0.0 ;
-tlogpost_i = tlogpost_i + (phi(i)-phi(i-1))*loglik ;
-for j=1:options_.posterior_sampler_options.HSsmc.nparticles
- qx = 0 ;
- q0 = 0 ;
- alpind = rand(1,1) ;
- validate= 0;
- while validate==0
- if alpind<options_.posterior_sampler_options.HSsmc.alp % RW, no need to modify qx and q0
- candidate = param(:,j) + cRchol*randn(size(param,1),1);
- elseif alpind<options_.posterior_sampler_options.HSsmc.alp + (1-options_.posterior_sampler_options.HSsmc.alp/2) % random walk with diagonal cov no need to modify qx and q0
- candidate = param(:,j) + cRdiagchol*randn(size(param,1),1);
- else % Proposal densities
- candidate = mu + cRchol*randn(size(param,1),1);
- qx = -.5*(candidate-mu)'*invR*(candidate-mu) ; % no need of the constants in the acceptation rule
- q0 = -.5*(param(:,j)-mu)'*invR*(param(:,j)-mu) ;
- end
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
- [tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,phi(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
- validate = 1;
- if rand(1,1)<exp((tlogpostx-qx)-(tlogpost_i(j)-q0)) % accept
- acpt = acpt + 1 ;
- param(:,j) = candidate;
- loglik(j) = loglikx;
- tlogpost_i(j) = tlogpostx;
- end
- end
- end
- end
-end
-acpt = acpt/options_.posterior_sampler_options.HSsmc.nparticles;
-
-function x = modified_logit(a,b,c,d)
-tmp = exp(c*d) ;
-x = a + b*tmp/(1 + tmp) ;
diff --git a/matlab/PlotPosteriorDistributions.m b/matlab/PlotPosteriorDistributions.m
index e280eee710dea9872ac236af834601d1d98a79ef..633ab1cf56a8363a581349259e6b7f9e891f070c 100644
--- a/matlab/PlotPosteriorDistributions.m
+++ b/matlab/PlotPosteriorDistributions.m
@@ -31,6 +31,7 @@ function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
latexDirectoryName = CheckPath('latex',M_.dname);
graphDirectoryName = CheckPath('graphs',M_.dname);
@@ -72,7 +73,7 @@ for i=1:npar
f1 = oo_.posterior_density.shocks_std.(name)(:,2);
oo_.prior_density.shocks_std.(name)(:,1) = x2;
oo_.prior_density.shocks_std.(name)(:,2) = f2;
- if ~options_.mh_posterior_mode_estimation
+ if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
pmod = oo_.posterior_mode.shocks_std.(name);
end
elseif i <= nvx+nvn
@@ -81,7 +82,7 @@ for i=1:npar
f1 = oo_.posterior_density.measurement_errors_std.(name)(:,2);
oo_.prior_density.measurement_errors_std.(name)(:,1) = x2;
oo_.prior_density.measurement_errors_std.(name)(:,2) = f2;
- if ~options_.mh_posterior_mode_estimation
+ if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
pmod = oo_.posterior_mode.measurement_errors_std.(name);
end
elseif i <= nvx+nvn+ncx
@@ -93,7 +94,7 @@ for i=1:npar
f1 = oo_.posterior_density.shocks_corr.(name)(:,2);
oo_.prior_density.shocks_corr.(name)(:,1) = x2;
oo_.prior_density.shocks_corr.(name)(:,2) = f2;
- if ~options_.mh_posterior_mode_estimation
+ if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
pmod = oo_.posterior_mode.shocks_corr.(name);
end
elseif i <= nvx+nvn+ncx+ncn
@@ -105,7 +106,7 @@ for i=1:npar
f1 = oo_.posterior_density.measurement_errors_corr.(name)(:,2);
oo_.prior_density.measurement_errors_corr.(name)(:,1) = x2;
oo_.prior_density.measurement_errors_corr.(name)(:,2) = f2;
- if ~options_.mh_posterior_mode_estimation
+ if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
pmod = oo_.posterior_mode.measurement_errors_corr.(name);
end
else
@@ -115,7 +116,7 @@ for i=1:npar
f1 = oo_.posterior_density.parameters.(name)(:,2);
oo_.prior_density.parameters.(name)(:,1) = x2;
oo_.prior_density.parameters.(name)(:,2) = f2;
- if ~options_.mh_posterior_mode_estimation
+ if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
pmod = oo_.posterior_mode.parameters.(name);
end
end
@@ -130,7 +131,7 @@ for i=1:npar
set(hh_plt, 'color', [0.7 0.7 0.7]);
hold on;
plot(x1, f1, '-k', 'linewidth', 2);
- if ~options_.mh_posterior_mode_estimation
+ if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
plot([pmod pmod], [0.0 1.1*top0], '--g', 'linewidth', 2);
end
box on
@@ -160,4 +161,4 @@ for i=1:npar
end
subplotnum = 0;
end
-end
\ No newline at end of file
+end
diff --git a/matlab/SMC_samplers_initialization.m b/matlab/SMC_samplers_initialization.m
deleted file mode 100644
index f144ab66bd8e7307166d4cd21d83a6cda9c5e88b..0000000000000000000000000000000000000000
--- a/matlab/SMC_samplers_initialization.m
+++ /dev/null
@@ -1,113 +0,0 @@
-function [ ix2, temperedlogpost, loglik, bayestopt_] = ...
- SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_, NumberOfParticles)
-% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfParticles, bayestopt_] = ...
-% SMC_samplers_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,NumberOfParticles)
-% Draw in prior distribution to initialize samplers.
-%
-% INPUTS
-% o TargetFun [char] string specifying the name of the objective
-% function (tempered posterior kernel and likelihood).
-% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
-% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
-% o dataset_ data structure
-% o dataset_info dataset info structure
-% o options_ options structure
-% o M_ model structure
-% o estim_params_ estimated parameters structure
-% o bayestopt_ estimation options structure
-% o oo_ outputs structure
-%
-% OUTPUTS
-% o ix2 [double] (NumberOfParticles*npar) vector of starting points for different chains
-% o ilogpo2 [double] (NumberOfParticles*1) vector of initial posterior values for different chains
-% o iloglik2 [double] (NumberOfParticles*1) vector of initial likelihood values for different chains
-% o ModelName [string] name of the mod-file
-% o MetropolisFolder [string] path to the Metropolis subfolder
-% o bayestopt_ [structure] estimation options structure
-%
-% SPECIAL REQUIREMENTS
-% None.
-
-% Copyright © 2006-2022 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-
-%Initialize outputs
-ix2 = [];
-ilogpo2 = [];
-iloglik2 = [];
-ModelName = [];
-MetropolisFolder = [];
-
-ModelName = M_.fname;
-if ~isempty(M_.bvar)
- ModelName = [ModelName '_bvar'];
-end
-
-MetropolisFolder = CheckPath('dsmh',M_.dname);
-BaseName = [MetropolisFolder filesep ModelName];
-
-npar = length(xparam1);
-
-% Here we start a new DS Metropolis-Hastings, previous draws are discarded.
-disp('Estimation:: Initialization...')
-% Delete old dsmh files if any...
-files = dir([BaseName '_dsmh*_blck*.mat']);
-%if length(files)
-% delete([BaseName '_dsmh*_blck*.mat']);
-% disp('Estimation::smc: Old dsmh-files successfully erased!')
-%end
-% Delete old log file.
-file = dir([ MetropolisFolder '/dsmh.log']);
-%if length(file)
-% delete([ MetropolisFolder '/dsmh.log']);
-% disp('Estimation::dsmh: Old dsmh.log file successfully erased!')
-% disp('Estimation::dsmh: Creation of a new dsmh.log file.')
-%end
-fidlog = fopen([MetropolisFolder '/dsmh.log'],'w');
-fprintf(fidlog,'%% DSMH log file (Dynare).\n');
-fprintf(fidlog,['%% ' datestr(now,0) '.\n']);
-fprintf(fidlog,' \n\n');
-fprintf(fidlog,'%% Session 1.\n');
-fprintf(fidlog,' \n');
-prior_draw(bayestopt_,options_.prior_trunc);
-% Find initial values for the NumberOfParticles chains...
-options_=set_dynare_seed_local_options(options_,'default');
-fprintf(fidlog,[' Initial values of the parameters:\n']);
-disp('Estimation::dsmh: Searching for initial values...');
-ix2 = zeros(npar,NumberOfParticles);
-temperedlogpost = zeros(NumberOfParticles,1);
-loglik = zeros(NumberOfParticles,1);
-%stderr = sqrt(bsxfun(@power,mh_bounds.ub-mh_bounds.lb,2)/12)/10;
-for j=1:NumberOfParticles
- validate = 0;
- while validate == 0
- candidate = prior_draw()';
-% candidate = xparam1(:) + 0.001*randn(npar,1);%bsxfun(@times,stderr,randn(npar,1)) ;
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
- ix2(:,j) = candidate ;
- [temperedlogpost(j),loglik(j)] = tempered_likelihood(TargetFun,candidate,0.0,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- if isfinite(loglik(j)) % if returned log-density is Inf or Nan (penalized value)
- validate = 1;
- end
- end
- end
-end
-fprintf(fidlog,' \n');
-disp('Estimation:: Initial values found!')
-skipline()
-
-
diff --git a/matlab/check_posterior_sampler_options.m b/matlab/check_posterior_sampler_options.m
index a3f127bd99c4250447cae319d58deff320270186..3c81972df91da529a5800f8ce3e73bc18b92d1d0 100644
--- a/matlab/check_posterior_sampler_options.m
+++ b/matlab/check_posterior_sampler_options.m
@@ -1,20 +1,17 @@
-function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_,outputFolderName)
-% function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_,outputFolderName)
-% initialization of posterior samplers
+function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_, outputFolderName)
+
+% Initialization of posterior samplers
%
% INPUTS
-% posterior_sampler_options: posterior sampler options
-% fname: name of the mod-file
-% dname: name of directory with metropolis folder
-% options_: structure storing the options
-% bounds: structure containing prior bounds
-% bayestopt_: structure storing information about priors
-
+% - posterior_sampler_options [struct] posterior sampler options
+% - options_ [struct] options
+% - bounds [struct] prior bounds
+% - bayestopt_ [struct] information about priors
+%
% OUTPUTS
-% posterior_sampler_options: checked posterior sampler options
-% options_: structure storing the options
-% bayestopt_: structure storing information about priors
-% outputFolderName: string of folder to store mat files
+% - posterior_sampler_options [struct] checked posterior sampler options (updated)
+% - options_ [struct] options (updated)
+% - bayestopt_ [struct] information about priors (updated)
%
% SPECIAL REQUIREMENTS
% none
@@ -40,15 +37,17 @@ if nargin < 7
outputFolderName = 'Output';
end
-init=0;
+init = false;
if isempty(posterior_sampler_options)
- init=1;
+ init = true;
end
if init
% set default options and user defined options
posterior_sampler_options.posterior_sampling_method = options_.posterior_sampler_options.posterior_sampling_method;
- posterior_sampler_options.bounds = bounds;
+ if ~issmc(options_)
+ posterior_sampler_options.bounds = bounds;
+ end
switch posterior_sampler_options.posterior_sampling_method
@@ -227,7 +226,6 @@ if init
end
end
-
case 'slice'
posterior_sampler_options.parallel_bar_refresh_rate=1;
posterior_sampler_options.serial_bar_refresh_rate=1;
@@ -342,50 +340,120 @@ if init
end
% moreover slice must be associated to:
- % options_.mh_posterior_mode_estimation = false;
- % this is done below, but perhaps preprocessing should do this?
+ % options_.mh_posterior_mode_estimation = false;
+ % this is done below, but perhaps preprocessing should do this?
- if ~isempty(posterior_sampler_options.mode)
- % multimodal case
- posterior_sampler_options.rotated = 1;
- posterior_sampler_options.WR=[];
- end
- % posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
+ if ~isempty(posterior_sampler_options.mode)
+ % multimodal case
+ posterior_sampler_options.rotated = 1;
+ posterior_sampler_options.WR=[];
+ end
+ % posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
- posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
- if options_.load_mh_file
- posterior_sampler_options.slice_initialize_with_mode = 0;
- else
- if ~posterior_sampler_options.slice_initialize_with_mode
- posterior_sampler_options.invhess=[];
+ posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
+ if options_.load_mh_file
+ posterior_sampler_options.slice_initialize_with_mode = 0;
+ else
+ if ~posterior_sampler_options.slice_initialize_with_mode
+ posterior_sampler_options.invhess=[];
+ end
end
- end
- if ~isempty(posterior_sampler_options.mode_files) % multimodal case
- modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
- load(modes, 'xparams')
- if size(xparams,2)<2
- error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
+ if ~isempty(posterior_sampler_options.mode_files) % multimodal case
+ modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
+ load(modes, 'xparams')
+ if size(xparams,2)<2
+ error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
+ end
+ for j=1:size(xparams,2)
+ mode(j).m=xparams(:,j);
+ end
+ posterior_sampler_options.mode = mode;
+ posterior_sampler_options.rotated = 1;
+ posterior_sampler_options.WR=[];
end
- for j=1:size(xparams,2)
- mode(j).m=xparams(:,j);
+
+ case 'hssmc'
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.hssmc);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'target'
+ posterior_sampler_options.target = options_list{i,2};
+ case 'steps'
+ posterior_sampler_options.steps = options_list{i,2};
+ case 'scale'
+ posterior_sampler_options.scale = options_list{i,2};
+ case 'particles'
+ posterior_sampler_options.particles = options_list{i,2};
+ case 'lambda'
+ posterior_sampler_options.lambda = options_list{i,2};
+ otherwise
+ warning(['hssmc: Unknown option (' options_list{i,1} ')!'])
+ end
+ end
+ end
+
+ options_.mode_compute = 0;
+ options_.cova_compute = 0;
+ options_.mh_replic = 0;
+ options_.mh_posterior_mode_estimation = false;
+
+ case 'dsmh'
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.dsmh);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
+ end
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+ end
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+ case 'number_of_particles'
+ posterior_sampler_options.particles = options_list{i,2};
+ otherwise
+ warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
+ end
end
- posterior_sampler_options.mode = mode;
- posterior_sampler_options.rotated = 1;
- posterior_sampler_options.WR=[];
end
- otherwise
- error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
- end
+ options_.mode_compute = 0;
+ options_.cova_compute = 0;
+ options_.mh_replic = 0;
+ options_.mh_posterior_mode_estimation = true;
+
+ otherwise
+ error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
+ end
- return
+ return
end
% here are all samplers requiring a proposal distribution
if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
- if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
+ if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
if strcmp('hessian',options_.MCMC_jumping_covariance)
skipline()
disp('check_posterior_sampler_options:: I cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed')
diff --git a/matlab/compute_mh_covariance_matrix.m b/matlab/compute_mh_covariance_matrix.m
index e7f0c6a5955d4bf6b259a7d3d41867c8974f32e0..e6af987f6c4efd03ead186e33cd084dd4df17992 100644
--- a/matlab/compute_mh_covariance_matrix.m
+++ b/matlab/compute_mh_covariance_matrix.m
@@ -1,23 +1,19 @@
-function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,fname,dname,outputFolderName)
-% function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,fname,dname,outputFolderName)
+function [mean, covariance, mode, kernel_at_the_mode] = compute_mh_covariance_matrix(names, fname, dname, outputFolderName)
+
% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
-% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
-% a file <fname>_mh_mode.mat.
+% estimated mode, using posterior draws from a metropolis-hastings.
%
% INPUTS
-% o bayestopt_ [struct] characterizing priors
-% o fname [string] name of model
-% o dname [string] name of directory with metropolis folder
-% o outputFolderName [string] name of directory to store results
+% - names [cell] n×1 cell array of row char arrays, names of the estimated parameters.
+% - fname [char] name of the model
+% - dname [char] name of subfolder with output files
+% - outputFolderName [char] name of directory to store results
%
% OUTPUTS
-% o posterior_mean [double] (n*1) vector, posterior expectation of the parameters.
-% o posterior_covariance [double] (n*n) matrix, posterior covariance of the parameters (computed from previous metropolis hastings).
-% o posterior_mode [double] (n*1) vector, posterior mode of the parameters.
-% o posterior_kernel_at_the_mode [double] scalar.
-%
-% SPECIAL REQUIREMENTS
-% None.
+% - mean [double] n×1 vector, posterior expectation of the parameters.
+% - covariance [double] n×n matrix, posterior covariance of the parameters.
+% - mode [double] n×1 vector, posterior mode of the parameters.
+% - kernel_at_the_mode [double] scalar, value of the posterior kernel at the mode.
% Copyright © 2006-2023 Dynare Team
%
@@ -35,9 +31,7 @@ function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-if nargin < 4
- outputFolderName = 'Output';
-end
+
MetropolisFolder = CheckPath('metropolis',dname);
BaseName = [MetropolisFolder filesep fname];
@@ -49,29 +43,24 @@ TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
[nblck, n] = size(record.LastParameters);
-posterior_kernel_at_the_mode = -Inf;
-posterior_mean = zeros(n,1);
-posterior_mode = NaN(n,1);
-posterior_covariance = zeros(n,n);
+kernel_at_the_mode = -Inf;
+mean = zeros(n,1);
+mode = NaN(n,1);
+covariance = zeros(n,n);
offset = 0;
for b=1:nblck
first_line = FirstLine;
for n = FirstMhFile:TotalNumberOfMhFiles
load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
- [tmp,idx] = max(logpo2);
- if tmp>posterior_kernel_at_the_mode
- posterior_kernel_at_the_mode = tmp;
- posterior_mode = x2(idx,:);
+ [tmp, idx] = max(logpo2);
+ if tmp>kernel_at_the_mode
+ kernel_at_the_mode = tmp;
+ mode = x2(idx,:);
end
- [posterior_mean,posterior_covariance,offset] = recursive_moments(posterior_mean,posterior_covariance,x2(first_line:end,:),offset);
+ [mean, covariance, offset] = recursive_moments(mean, covariance, x2(first_line:end,:), offset);
first_line = 1;
end
end
-xparam1 = posterior_mode';
-hh = inv(posterior_covariance);
-fval = posterior_kernel_at_the_mode;
-parameter_names = bayestopt_.name;
-
-save([dname filesep outputFolderName filesep fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');
\ No newline at end of file
+mode = transpose(mode);
diff --git a/matlab/compute_posterior_covariance_matrix.m b/matlab/compute_posterior_covariance_matrix.m
new file mode 100644
index 0000000000000000000000000000000000000000..7fe0ca23590db4b422d894dc4e0cbe53e4dd314f
--- /dev/null
+++ b/matlab/compute_posterior_covariance_matrix.m
@@ -0,0 +1,65 @@
+function [mu, covariance, mode, kernel_at_the_mode] = compute_posterior_covariance_matrix(names, fname, dname, options_, outputFolderName)
+
+% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
+% estimated mode, using posterior draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
+% a file <fname>_mh_mode.mat, hssmc_mode.mat or dsmh__mode.mat under <dname>/<outputFolderName>/.
+%
+% INPUTS
+% - names [cell] n×1 cell array of row char arrays, names of the estimated parameters.
+% - fname [char] name of the model
+% - dname [char] name of subfolder with output files
+% - outputFolderName [char] name of directory to store results
+%
+% OUTPUTS
+% - mean [double] n×1 vector, posterior expectation of the parameters.
+% - covariance [double] n×n matrix, posterior covariance of the parameters.
+% - mode [double] n×1 vector, posterior mode of the parameters.
+% - kernel_at_the_mode [double] scalar, value of the posterior kernel at the mode.
+
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+
+if nargin<5
+ outputFolderName = 'Output';
+end
+
+if ishssmc(options_)
+ % Load draws from the posterior distribution
+ pfiles = dir(sprintf('%s/hssmc/particles-*.mat', dname));
+ posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', dname, length(pfiles), length(pfiles)));
+ % Get the posterior mode
+ [kernel_at_the_mode, id] = max(posterior.tlogpostkernel);
+ mode = posterior.particles(:,id);
+ % Compute the posterior mean
+ mu = sum(posterior.particles, 2)/length(posterior.tlogpostkernel);
+ % Compute the posterior covariance
+ covariance = (posterior.particles-mu)*(posterior.particles-mu)'/length(posterior.tlogpostkernel);
+else
+ [mu, covariance, mode, kernel_at_the_mode] = compute_mh_covariance_matrix(names, fname, dname, outputFolderName);
+end
+
+xparam1 = mode;
+hh = inv(covariance);
+fval = kernel_at_the_mode;
+parameter_names = names;
+
+if ishssmc(options_)
+ save(sprintf('%s/%s/hssmc_mode.mat', dname, outputFolderName), 'xparam1', 'hh', 'fval', 'parameter_names');
+else
+ save(sprintf('%s/%s/%s_mh_mode.mat', dname, outputFolderName, fname), 'xparam1', 'hh', 'fval', 'parameter_names');
+end
diff --git a/matlab/convergence_diagnostics/mcmc_diagnostics.m b/matlab/convergence_diagnostics/mcmc_diagnostics.m
index 25ca6b34b7797e7470346aff97c5bd45d4529c13..1c1b1abb72f91cc1c6d8f9f873cbe3b26b564e95 100644
--- a/matlab/convergence_diagnostics/mcmc_diagnostics.m
+++ b/matlab/convergence_diagnostics/mcmc_diagnostics.m
@@ -79,7 +79,7 @@ for jj = 1:npar
par_name_temp = get_the_name(jj, options_.TeX, M_, estim_params_, options_.varobs);
param_name = vertcat(param_name, par_name_temp);
end
- Draws = GetAllPosteriorDraws(M_.dname, M_.fname, jj, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
+ Draws = GetAllPosteriorDraws(options_, M_.dname, M_.fname, jj, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
Draws = reshape(Draws, [NumberOfDraws nblck]);
Nc = min(1000, NumberOfDraws/2);
for ll = 1:nblck
diff --git a/matlab/default_option_values.m b/matlab/default_option_values.m
index cbaa775a230bd507e5e9858ae9c9c63186959de6..b3a500c26e1880424f36c56c67725f160c33fc59 100644
--- a/matlab/default_option_values.m
+++ b/matlab/default_option_values.m
@@ -451,6 +451,7 @@ options_.nk = 1;
options_.noconstant = false;
options_.nodiagnostic = false;
options_.mh_posterior_mode_estimation = false;
+options_.smc_posterior_mode_estimation = false;
options_.prefilter = 0;
options_.presample = 0;
options_.prior_trunc = 1e-10;
@@ -463,7 +464,7 @@ options_.use_mh_covariance_matrix = false;
options_.gradient_method = 2; %used by csminwel and newrat
options_.gradient_epsilon = 1e-6; %used by csminwel and newrat
options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers
- % Random Walk Metropolis-Hastings
+% Random Walk Metropolis-Hastings
options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings';
options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal';
options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3;
@@ -491,23 +492,19 @@ options_.posterior_sampler_options.imh.proposal_distribution = 'rand_multivariat
options_.posterior_sampler_options.imh.use_mh_covariance_matrix=0;
options_.posterior_sampler_options.imh.save_tmp_file=0;
% Herbst and Schorfeide SMC Sampler
-%options_.posterior_sampler = 'Herbst_Schorfheide' ;
-options_.posterior_sampler_options.HSsmc.nphi= 25 ;
-options_.posterior_sampler_options.HSsmc.lambda = 2 ;
-options_.posterior_sampler_options.HSsmc.nparticles = 20000 ;
-options_.posterior_sampler_options.HSsmc.c = 0.5 ;
-options_.posterior_sampler_options.HSsmc.acpt = 0.25 ;
-options_.posterior_sampler_options.HSsmc.trgt = 0.25 ;
-options_.posterior_sampler_options.HSsmc.option_mutation = 1 ;
-options_.posterior_sampler_options.HSsmc.alp = .9 ;
+options_.posterior_sampler_options.hssmc.steps= 25;
+options_.posterior_sampler_options.hssmc.lambda = 2;
+options_.posterior_sampler_options.hssmc.particles = 20000;
+options_.posterior_sampler_options.hssmc.scale = 0.5;
+options_.posterior_sampler_options.hssmc.acpt = 1.00;
+options_.posterior_sampler_options.hssmc.target = 0.25;
% DSMH: Dynamic Striated Metropolis-Hastings algorithm
-%options_.posterior_sampler = 'DSMH' ;
options_.posterior_sampler_options.dsmh.H = 25 ;
options_.posterior_sampler_options.dsmh.N = 20 ;
options_.posterior_sampler_options.dsmh.G = 10 ;
options_.posterior_sampler_options.dsmh.K = 50 ;
options_.posterior_sampler_options.dsmh.lambda1 = 0.1 ;
-options_.posterior_sampler_options.dsmh.nparticles = 20000 ;
+options_.posterior_sampler_options.dsmh.particles = 20000 ;
options_.posterior_sampler_options.dsmh.alpha0 = 0.2 ;
options_.posterior_sampler_options.dsmh.alpha1 = 0.3 ;
options_.posterior_sampler_options.dsmh.tau = 10 ;
diff --git a/matlab/dprintf.m b/matlab/dprintf.m
index c47ed1856ab0c0241fbd4dec0c772bd90f687af8..e122f72fae01bed2cef50893fa1247a018a453e3 100644
--- a/matlab/dprintf.m
+++ b/matlab/dprintf.m
@@ -17,4 +17,4 @@ function dprintf(str, varargin)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-disp(sprintf(str, varargin{:}));
\ No newline at end of file
+disp(sprintf(str, varargin{:}));
diff --git a/matlab/dynare_config.m b/matlab/dynare_config.m
index b0ee922517fc4daabb4d897bf1d31e3630f6ed1a..13f0d5c84c56f0d820f03c7f348fbfef6e044b50 100644
--- a/matlab/dynare_config.m
+++ b/matlab/dynare_config.m
@@ -51,6 +51,9 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
'/discretionary_policy/' ; ...
'/distributions/' ; ...
'/ep/' ; ...
+ '/estimation/'; ...
+ '/estimation/smc/'; ...
+ '/estimation/resampler/'; ...
'/gsa/' ; ...
'/kalman/' ; ...
'/kalman/likelihood' ; ...
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 990bd3775f9ea1ef10045bbe110f895bda6ef4b2..40206b4d058563e7efb6167bf62ce3b7f2153b17 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -31,6 +31,14 @@ function dynare_estimation_1(var_list_,dname)
global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
+if issmc(options_)
+ options_.mode_compute = 0;
+ options_.mh_replic = 0;
+ options_.mh_recover = false;
+ options_.load_mh_file = false;
+ options_.load_results_after_load_mh = false;
+end
+
dispString = 'Estimation::mcmc';
if ~exist([M_.dname filesep 'Output'],'dir')
@@ -88,7 +96,9 @@ if options_.order > 1
end
end
-%% set objective function
+%
+% set objective function
+%
if ~options_.dsge_var
if options_.particle.status
objective_function = str2func('non_linear_dsge_likelihood');
@@ -147,7 +157,10 @@ if ~isempty(estim_params_)
M_ = set_all_parameters(xparam1,estim_params_,M_);
end
-%% perform initial estimation checks;
+%
+% perform initial estimation checks;
+%
+
try
oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,bounds,oo_);
catch % if check fails, provide info on using calibration if present
@@ -164,8 +177,11 @@ catch % if check fails, provide info on using calibration if present
rethrow(e);
end
-%% Run smoother if no estimation or mode-finding are requested
-if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
+%
+% Run smoother if no estimation or mode-finding are requested
+%
+
+if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation && ~issmc(options_)
if options_.order==1 && ~options_.particle.status
if options_.smoother
if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
@@ -210,11 +226,11 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.
end
end
-%% Estimation of the posterior mode or likelihood mode
-
-
+%
+% Estimation of the posterior mode or likelihood mode
+%
-if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
+if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation && ~issmc(options_)
optimizer_vec = [options_.mode_compute;num2cell(options_.additional_optimizer_steps)];
for optim_iter = 1:length(optimizer_vec)
current_optimizer = optimizer_vec{optim_iter};
@@ -238,7 +254,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2;
[~,~,~,~,hh] = feval(objective_function,xparam1, ...
- dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+ dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(current_optimizer) || ~(isequal(current_optimizer,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
% enter here if i) not mode_compute_5, ii) if mode_compute_5 and newratflag==1;
@@ -292,16 +308,19 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
end
-if ~options_.mh_posterior_mode_estimation && options_.cova_compute
+if ~options_.mh_posterior_mode_estimation && options_.cova_compute && ~issmc(options_)
check_hessian_at_the_mode(hh, xparam1, M_, estim_params_, options_, bounds);
end
-%% create mode_check_plots
-if options_.mode_check.status && ~options_.mh_posterior_mode_estimation
+%
+% create mode_check_plots
+%
+
+if options_.mode_check.status && ~options_.mh_posterior_mode_estimation && ~issmc(options_)
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 0;
mode_check(objective_function,xparam1,hh,options_,M_,estim_params_,bayestopt_,bounds,false,...
- dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+ dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
options_.analytic_derivation = ana_deriv_old;
end
@@ -309,43 +328,38 @@ oo_.posterior.optimization.mode = [];
oo_.posterior.optimization.Variance = [];
oo_.posterior.optimization.log_density=[];
-invhess=[];
-if ~options_.mh_posterior_mode_estimation
- oo_.posterior.optimization.mode = xparam1;
- if exist('fval','var')
- oo_.posterior.optimization.log_density=-fval;
- end
- if options_.cova_compute
- hsd = sqrt(diag(hh));
- invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- stdh = sqrt(diag(invhess));
- oo_.posterior.optimization.Variance = invhess;
+invhess = [];
+
+if ~issmc(options_)
+ if ~options_.mh_posterior_mode_estimation
+ oo_.posterior.optimization.mode = xparam1;
+ if exist('fval','var')
+ oo_.posterior.optimization.log_density=-fval;
+ end
+ if options_.cova_compute
+ hsd = sqrt(diag(hh));
+ invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
+ stdh = sqrt(diag(invhess));
+ oo_.posterior.optimization.Variance = invhess;
+ end
+ else
+ variances = bayestopt_.p2.*bayestopt_.p2;
+ idInf = isinf(variances);
+ variances(idInf) = 1;
+ invhess = options_.mh_posterior_mode_estimation*diag(variances);
+ xparam1 = bayestopt_.p5;
+ idNaN = isnan(xparam1);
+ xparam1(idNaN) = bayestopt_.p1(idNaN);
+ outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
+ xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
end
-else
- variances = bayestopt_.p2.*bayestopt_.p2;
- idInf = isinf(variances);
- variances(idInf) = 1;
- invhess = options_.mh_posterior_mode_estimation*diag(variances);
- xparam1 = bayestopt_.p5;
- idNaN = isnan(xparam1);
- xparam1(idNaN) = bayestopt_.p1(idNaN);
- outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
- xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
end
if ~options_.cova_compute
stdh = NaN(length(xparam1),1);
end
-if options_.particle.status && isfield(options_.particle,'posterior_sampler')
- if strcmpi(options_.particle.posterior_sampler,'Herbst_Schorfheide')
- Herbst_Schorfheide_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state)
- elseif strcmpi(options_.particle.posterior_sampler,'DSMH')
- DSMH_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state)
- end
-end
-
-if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
+if ~issmc(options_) && any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
% display results table and store parameter estimates and standard errors in results
oo_ = display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Posterior', 'posterior');
% Laplace approximation to the marginal log density:
@@ -366,56 +380,75 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
[~,~,~,~,~,~,~,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
-
-elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
+elseif ~issmc(options_) && ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
oo_=display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Maximum Likelihood', 'mle');
end
-invhess = set_mcmc_jumping_covariance(invhess, nx, options_.MCMC_jumping_covariance, bayestopt_, 'dynare_estimation_1');
-
-if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
- (any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
- %reset bounds as lb and ub must only be operational during mode-finding
- bounds = set_mcmc_prior_bounds(xparam1, bayestopt_, options_, 'dynare_estimation_1');
- % Tunes the jumping distribution's scale parameter
- if options_.mh_tune_jscale.status
- if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
- options_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds,...
- dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
- bayestopt_.jscale(:) = options_.mh_jscale;
- fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale));
- else
- warning('mh_tune_jscale is only available with Random Walk Metropolis Hastings!')
+if ~issmc(options_)
+ invhess = set_mcmc_jumping_covariance(invhess, nx, options_.MCMC_jumping_covariance, bayestopt_, 'dynare_estimation_1');
+end
+
+%
+% Run SMC sampler.
+%
+
+if ishssmc(options_)
+ [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options([], M_.fname, M_.dname, options_, bounds, bayestopt_);
+ options_.posterior_sampler_options.current_options = posterior_sampler_options;
+ oo_.MarginalDensity.hssmc = hssmc(objective_function, bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
+elseif isdsmh(options_)
+ dsmh(objective_function, xparam1, bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
+end
+
+%
+% Run MCMC and compute posterior statistics.
+%
+
+if issmc(options_) || (any(bayestopt_.pshape>0) && options_.mh_replic) || (any(bayestopt_.pshape>0) && options_.load_mh_file) % not ML estimation
+ if ~issmc(options_)
+ % Reset bounds as lb and ub must only be operational during mode-finding
+ bounds = set_mcmc_prior_bounds(xparam1, bayestopt_, options_, 'dynare_estimation_1');
+ % Tune the jumping distribution's scale parameter
+ if options_.mh_tune_jscale.status
+ if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
+ options_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds,...
+ dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+ bayestopt_.jscale(:) = options_.mh_jscale;
+ fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale));
+ else
+ warning('mh_tune_jscale is only available with Random Walk Metropolis Hastings!')
+ end
end
- end
- % runs MCMC
- if options_.mh_replic || options_.load_mh_file
- posterior_sampler_options = options_.posterior_sampler_options.current_options;
- posterior_sampler_options.invhess = invhess;
- [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, M_.fname, M_.dname, options_, bounds, bayestopt_);
- % store current options in global
- options_.posterior_sampler_options.current_options = posterior_sampler_options;
- if options_.mh_replic
- ana_deriv_old = options_.analytic_derivation;
- options_.analytic_derivation = 0;
- posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,dispString);
- options_.analytic_derivation = ana_deriv_old;
+ % Run MCMC
+ if options_.mh_replic || options_.load_mh_file
+ posterior_sampler_options = options_.posterior_sampler_options.current_options;
+ posterior_sampler_options.invhess = invhess;
+ [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, M_.fname, M_.dname, options_, bounds, bayestopt_);
+ % store current options in global
+ options_.posterior_sampler_options.current_options = posterior_sampler_options;
+ if options_.mh_replic
+ ana_deriv_old = options_.analytic_derivation;
+ options_.analytic_derivation = 0;
+ posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,dispString);
+ options_.analytic_derivation = ana_deriv_old;
+ end
end
+ % Discard first mh_drop percent of the draws:
+ CutSample(M_, options_, dispString);
end
- %% Here I discard first mh_drop percent of the draws:
- CutSample(M_, options_, dispString);
- if options_.mh_posterior_mode_estimation
- [~,~,posterior_mode,~] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname);
- oo_=fill_mh_mode(posterior_mode',NaN(length(posterior_mode),1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
+ if options_.mh_posterior_mode_estimation || (issmc(options_) && options_.smc_posterior_mode_estimation)
+ [~, covariance, posterior_mode, ~] = compute_posterior_covariance_matrix(bayestopt_.name, M_.fname, M_.dname, options_);
+ oo_ = fill_mh_mode(posterior_mode, sqrt(diag(covariance)), M_, options_, estim_params_, oo_, 'posterior');
%reset qz_criterium
- options_.qz_criterium=qz_criterium_old;
+ options_.qz_criterium = qz_criterium_old;
return
else
- %get stored results if required
- if options_.load_mh_file && options_.load_results_after_load_mh
- oo_load_mh=load([M_.dname filesep 'Output' filesep M_.fname '_results'],'oo_');
+ % Get stored results if required
+ if ~issmc(options_) && options_.load_mh_file && options_.load_results_after_load_mh
+ oo_load_mh = load(sprintf('%s/%s/%s_results', M_.dname, 'Output', M_.fname), 'oo_');
end
- if ~options_.nodiagnostic
+ % Compute MCMC convergence diagnostics
+ if ~issmc(options_) && ~options_.nodiagnostic
if (options_.mh_replic>0 || (options_.load_mh_file && ~options_.load_results_after_load_mh))
oo_= mcmc_diagnostics(options_, estim_params_, M_,oo_);
elseif options_.load_mh_file && options_.load_results_after_load_mh
@@ -424,9 +457,11 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
end
end
end
- %% Estimation of the marginal density from the Mh draws:
- if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
- [~,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+ % Estimation of the marginal density from the Mh draws:
+ if ishssmc(options_) || options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
+ if ~issmc(options_)
+ [~, oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+ end
% Store posterior statistics by parameter name
oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, prior_dist_names);
if ~options_.nograph
@@ -435,13 +470,13 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
% Store posterior mean in a vector and posterior variance in
% a matrix
[oo_.posterior.metropolis.mean,oo_.posterior.metropolis.Variance] ...
- = GetPosteriorMeanVariance(M_,options_.mh_drop);
+ = GetPosteriorMeanVariance(options_, M_);
elseif options_.load_mh_file && options_.load_results_after_load_mh
- %% load fields from previous MCMC run stored in results-file
+ % load fields from previous MCMC run stored in results-file
field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
- 'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
- 'prior_density',...%fields set by PlotPosteriorDistributions
- };
+ 'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
+ 'prior_density',...%fields set by PlotPosteriorDistributions
+ };
for field_iter=1:size(field_names,2)
if isfield(oo_load_mh.oo_,field_names{1,field_iter})
oo_.(field_names{1,field_iter})=oo_load_mh.oo_.(field_names{1,field_iter});
@@ -456,7 +491,9 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis;
end
end
- [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
+ if ~issmc(options_)
+ [error_flag, ~, options_]= metropolis_draw(1, options_, estim_params_, M_);
+ end
if ~(~isempty(options_.sub_draws) && options_.sub_draws==0)
if options_.bayesian_irf
if error_flag
@@ -499,9 +536,9 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
error('%s: Particle Smoothers are not yet implemented.',dispString)
end
end
- else
- fprintf('%s: sub_draws was set to 0. Skipping posterior computations.',dispString);
- end
+ else
+ fprintf('%s: sub_draws was set to 0. Skipping posterior computations.',dispString);
+ end
xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
M_ = set_all_parameters(xparam1,estim_params_,M_);
end
@@ -517,7 +554,7 @@ end
%Run and store classical smoother if needed
if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)) ...
- || ~options_.smoother ) && ~options_.partial_information % to be fixed
+ || ~options_.smoother ) && ~options_.partial_information % to be fixed
%% ML estimation, or posterior mode without Metropolis-Hastings or Metropolis without Bayesian smoothed variables
oo_=save_display_classical_smoother_results(xparam1,M_,oo_,options_,bayestopt_,dataset_,dataset_info,estim_params_);
end
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index 4932e304262a4594c202a30a65112ecc55240014..76fa750ffb741bfc79a6cb5e290409889e70719a 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -1,8 +1,6 @@
function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, bounds] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
-% function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, bounds] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
-% performs initialization tasks before estimation or
-% global sensitivity analysis
+% Performs initialization tasks before estimation or global sensitivity analysis
%
% INPUTS
% var_list_: selected endogenous variables vector
@@ -390,7 +388,7 @@ end
%set options for old interface from the ones for new interface
if ~isempty(dataset_)
options_.nobs = dataset_.nobs;
- if options_.endogenous_prior
+ if options_.endogenous_prior
if ~isnan(dataset_info.missing.number_of_observations) && ~(dataset_info.missing.number_of_observations==0) %missing observations present
if dataset_info.missing.no_more_missing_observations<dataset_.nobs-10
fprintf('\ndynare_estimation_init: There are missing observations in the data.\n')
@@ -537,15 +535,15 @@ end
if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
if ~isempty(options_.nk)
- fprintf('dynare_estimation_init: the inversion filter does not support filter_step_ahead. Disabling the option.\n')
+ fprintf('dynare_estimation_init: the inversion filter does not support filter_step_ahead. Disabling the option.\n')
options_.nk=[];
end
if options_.filter_covariance
- fprintf('dynare_estimation_init: the inversion filter does not support filter_covariance. Disabling the option.\n')
+ fprintf('dynare_estimation_init: the inversion filter does not support filter_covariance. Disabling the option.\n')
options_.filter_covariance=false;
end
if options_.smoothed_state_uncertainty
- fprintf('dynare_estimation_init: the inversion filter does not support smoothed_state_uncertainty. Disabling the option.\n')
+ fprintf('dynare_estimation_init: the inversion filter does not support smoothed_state_uncertainty. Disabling the option.\n')
options_.smoothed_state_uncertainty=false;
end
end
diff --git a/matlab/estimation/resampler/kitagawa.m b/matlab/estimation/resampler/kitagawa.m
new file mode 100644
index 0000000000000000000000000000000000000000..f0823215289d697c496ae34c2e69227571986316
--- /dev/null
+++ b/matlab/estimation/resampler/kitagawa.m
@@ -0,0 +1,45 @@
+function indices = kitagawa(weights, noise)
+
+% Return indices for resampling.
+%
+% INPUTS
+% - weights [double] n×1 vector of partcles' weights.
+% - noise [double] scalar, uniform random deviates in [0,1]
+%
+% OUTPUTS
+% - indices [integer] n×1 vector of indices in [1:n]
+
+% Copyright © 2022-2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+n= length(weights);
+
+if nargin<2, noise = rand; end
+
+indices = NaN(n, 1);
+
+cweights = cumsum(weights);
+
+wweights = (transpose(0:n-1)+noise)*(1.0/n);
+
+j = 1;
+for i=1:n
+ while wweights(i)>cweights(j)
+ j = j+1;
+ end
+ indices(i) = j;
+end
diff --git a/matlab/DSMH_sampler.m b/matlab/estimation/smc/dsmh.m
similarity index 93%
rename from matlab/DSMH_sampler.m
rename to matlab/estimation/smc/dsmh.m
index 1c91c3a1ea192c8aaa04743f423aecab2a08d9d9..5382639d598176c03aba2bbf2dbef2c216bdd4ec 100644
--- a/matlab/DSMH_sampler.m
+++ b/matlab/estimation/smc/dsmh.m
@@ -1,5 +1,5 @@
-function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
+function dsmh(TargetFun, xparam1, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
+
% Dynamic Striated Metropolis-Hastings algorithm.
%
% INPUTS
@@ -33,7 +33,7 @@ function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_
% Then the comments write here can be used for all the other pairs of
% parallel functions and also for management functions.
-% Copyright © 2006-2023 Dynare Team
+% Copyright © 2022-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -50,14 +50,15 @@ function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+opts = options_.posterior_sampler_options.dsmh;
lambda = exp(bsxfun(@minus,options_.posterior_sampler_options.dsmh.H,1:1:options_.posterior_sampler_options.dsmh.H)/(options_.posterior_sampler_options.dsmh.H-1)*log(options_.posterior_sampler_options.dsmh.lambda1));
c = 0.055 ;
MM = int64(options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.G/10) ;
% Step 0: Initialization of the sampler
-[ param, tlogpost_iminus1, loglik, bayestopt_] = ...
- SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,options_.posterior_sampler_options.dsmh.nparticles);
+[param, tlogpost_iminus1, loglik, bayestopt_] = ...
+ smc_samplers_initialization(TargetFun, 'dsmh', opts.particles, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
ESS = zeros(options_.posterior_sampler_options.dsmh.H,1) ;
zhat = 1 ;
@@ -78,20 +79,17 @@ end
weights = exp(loglik*(lambda(end)-lambda(end-1)));
weights = weights/sum(weights);
-indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.nparticles);
+indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.particles);
distrib_param = param(:,indx_resmpl);
mean_xparam = mean(distrib_param,2);
npar = length(xparam1);
-%mat_var_cov = bsxfun(@minus,distrib_param,mean_xparam) ;
-%mat_var_cov = (mat_var_cov*mat_var_cov')/(options_.HSsmc.nparticles-1) ;
-%std_xparam = sqrt(diag(mat_var_cov)) ;
lb95_xparam = zeros(npar,1) ;
ub95_xparam = zeros(npar,1) ;
for i=1:npar
temp = sortrows(distrib_param(i,:)') ;
- lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.dsmh.nparticles) ;
- ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.dsmh.nparticles) ;
+ lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.dsmh.particles) ;
+ ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.dsmh.particles) ;
end
TeX = options_.TeX;
@@ -130,9 +128,9 @@ for k=1:npar %min(nstar,npar-(plt-1)*nstar)
subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
%kk = (plt-1)*nstar+k;
[name,texname] = get_the_name(k,TeX,M_,estim_params_,options_.varobs);
- optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.dsmh.nparticles,bandwidth,kernel_function);
+ optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.dsmh.particles,bandwidth,kernel_function);
[density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
- options_.posterior_sampler_options.dsmh.nparticles,optimal_bandwidth,kernel_function);
+ options_.posterior_sampler_options.dsmh.particles,optimal_bandwidth,kernel_function);
plot(density(:,1),density(:,2));
hold on
if TeX
diff --git a/matlab/estimation/smc/hssmc.m b/matlab/estimation/smc/hssmc.m
new file mode 100644
index 0000000000000000000000000000000000000000..59a0bd7045aba16a4e9fd2aabb6d21d18fa636ae
--- /dev/null
+++ b/matlab/estimation/smc/hssmc.m
@@ -0,0 +1,136 @@
+function mdd = hssmc(TargetFun, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
+
+% Sequential Monte-Carlo sampler, Herbst and Schorfheide (JAE, 2014).
+%
+% INPUTS
+% - TargetFun [char] string specifying the name of the objective function (posterior kernel).
+% - xparam1 [double] p×1 vector of parameters to be estimated (initial values).
+% - mh_bounds [double] p×2 matrix defining lower and upper bounds for the parameters.
+% - dataset_ [dseries] sample
+% - dataset_info [struct] informations about the dataset
+% - options_ [struct] dynare's options
+% - M_ [struct] model description
+% - estim_params_ [struct] estimated parameters
+% - bayestopt_ [struct] estimated parameters
+% - oo_ [struct] outputs
+%
+% SPECIAL REQUIREMENTS
+% None.
+
+% Copyright © 2022-2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+ smcopt = options_.posterior_sampler_options.current_options;
+
+ % Set location for the simulated particles.
+ SimulationFolder = CheckPath('hssmc', M_.dname);
+
+ % Define prior distribution
+ Prior = dprior(bayestopt_, options_.prior_trunc);
+
+ % Set function handle for the objective
+ eval(sprintf('%s = @(x) %s(x, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, mh_bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, []);', 'funobj', func2str(TargetFun)));
+
+ mlogit = @(x) .95 + .1/(1 + exp(-16*x)); % Update of the scale parameter
+
+ % Create the tempering schedule
+ phi = ((0:smcopt.steps-1)/(smcopt.steps-1)).^smcopt.lambda;
+
+ % Initialise the estimate of the marginal density of the data
+ mdd = .0;
+
+ % tuning for MH algorithms matrices
+ scl = zeros(smcopt.steps, 1); % scale parameter
+ ESS = zeros(smcopt.steps, 1); % ESS
+ acpt = zeros(smcopt.steps, 1); % average acceptance rate
+
+ % Initialization of the sampler (draws from the prior distribution with finite logged likelihood)
+ t0 = tic;
+ [particles, tlogpostkernel, loglikelihood] = ...
+ smc_samplers_initialization(funobj, 'hssmc', smcopt.particles, Prior, SimulationFolder, smcopt.steps);
+ tt = toc(t0);
+
+ dprintf('#Iter. lambda ESS Acceptance rate scale resample seconds')
+ dprintf('%3u %5.4f %9.5E %5.4f %4.3f %3s %5.2f', 1, 0, 0, 0, 0, 'no', tt)
+
+ weights = ones(smcopt.particles, 1)/smcopt.particles;
+
+ resampled_particle_swarm = false;
+
+ for i=2:smcopt.steps % Loop over the weight on the liklihood (phi)
+ weights = weights.*exp((phi(i)-phi(i-1))*loglikelihood);
+ sweight = sum(weights);
+ weights = weights/sweight;
+ mdd = mdd + log(sweight);
+ ESS(i) = 1.0/sum(weights.^2);
+ if (2*ESS(i) < smcopt.particles) % Resampling
+ resampled_particle_swarm = true;
+ iresample = kitagawa(weights);
+ particles = particles(:,iresample);
+ loglikelihood = loglikelihood(iresample);
+ tlogpostkernel = tlogpostkernel(iresample);
+ weights = ones(smcopt.particles, 1)/smcopt.particles;
+ end
+ smcopt.scale = smcopt.scale*mlogit(smcopt.acpt-smcopt.target); % Adjust the scale parameter
+ scl(i) = smcopt.scale; % Scale parameter (for the jumping distribution in MH mutation step).
+ mu = particles*weights; % Weighted average of the particles.
+ z = particles-mu;
+ R = z*(z'.*weights); % Weighted covariance matrix of the particles.
+ t0 = tic;
+ acpt_ = false(smcopt.particles, 1);
+ tlogpostkernel = tlogpostkernel + (phi(i)-phi(i-1))*loglikelihood;
+ [acpt_, particles, loglikelihood, tlogpostkernel] = ...
+ randomwalk(funobj, chol(R, 'lower'), mu, scl(i), phi(i), acpt_, Prior, particles, loglikelihood, tlogpostkernel);
+ smcopt.acpt = sum(acpt_)/smcopt.particles; % Acceptance rate.
+ tt = toc(t0);
+ acpt(i) = smcopt.acpt;
+ if resampled_particle_swarm
+ dprintf('%3u %5.4f %9.5E %5.4f %4.3f %3s %5.2f', i, phi(i), ESS(i), acpt(i), scl(i), 'yes', tt)
+ else
+ dprintf('%3u %5.4f %9.5E %5.4f %4.3f %3s %5.2f', i, phi(i), ESS(i), acpt(i), scl(i), 'no', tt)
+ end
+ if i==smcopt.steps
+ iresample = kitagawa(weights);
+ particles = particles(:,iresample);
+ end
+ save(sprintf('%s%sparticles-%u-%u.mat', SimulationFolder, filesep(), i, smcopt.steps), 'particles', 'tlogpostkernel', 'loglikelihood')
+ resampled_particle_swarm = false;
+ end
+
+end
+
+function [accept, particles, loglikelihood, tlogpostkernel] = randomwalk(funobj, RR, mu, scale, phi, accept, Prior, particles, loglikelihood, tlogpostkernel)
+
+ parfor j=1:size(particles, 2)
+ notvalid= true;
+ while notvalid
+ candidate = particles(:,j) + scale*(RR*randn(size(mu)));
+ if Prior.admissible(candidate)
+ [tlogpost, loglik] = tempered_likelihood(funobj, candidate, phi, Prior);
+ if isfinite(loglik)
+ notvalid = false;
+ if rand<exp(tlogpost-tlogpostkernel(j))
+ accept(j) = true ;
+ particles(:,j) = candidate;
+ loglikelihood(j) = loglik;
+ tlogpostkernel(j) = tlogpost;
+ end
+ end
+ end
+ end
+ end
+end
diff --git a/matlab/smc_resampling.m b/matlab/estimation/smc/isdsmh.m
similarity index 63%
rename from matlab/smc_resampling.m
rename to matlab/estimation/smc/isdsmh.m
index 4518f2c2dcafdacec359924bf128a307f12b143e..c05d3d496031c932ad0d8688469bb72f6bcc3045 100644
--- a/matlab/smc_resampling.m
+++ b/matlab/estimation/smc/isdsmh.m
@@ -1,7 +1,6 @@
-function indx = smc_resampling(weights,noise,number)
-% function indx = smc_resampling(weights,noise,number)
+function bool = isdsmh(options_)
-% Copyright © 2022 Dynare Team
+% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -18,13 +17,9 @@ function indx = smc_resampling(weights,noise,number)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
- indx = zeros(number,1);
- cumweights = cumsum(weights);
- randvec = (transpose(1:number)-1+noise(:))/number;
- j = 1;
- for i=1:number
- while (randvec(i)>cumweights(j))
- j = j+1;
- end
- indx(i) = j;
+bool = false;
+if isfield(options_, 'posterior_sampler_options')
+ if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'dsmh')
+ bool = true;
end
+end
diff --git a/matlab/estimation/smc/ishssmc.m b/matlab/estimation/smc/ishssmc.m
new file mode 100644
index 0000000000000000000000000000000000000000..6d2820d4435c1cc05af43f9855d1c10422507ba5
--- /dev/null
+++ b/matlab/estimation/smc/ishssmc.m
@@ -0,0 +1,25 @@
+function bool = ishssmc(options_)
+
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+bool = false;
+if isfield(options_, 'posterior_sampler_options')
+ if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'hssmc')
+ bool = true;
+ end
+end
diff --git a/matlab/estimation/smc/smc_samplers_initialization.m b/matlab/estimation/smc/smc_samplers_initialization.m
new file mode 100644
index 0000000000000000000000000000000000000000..fb27f251e74768e8dbebbdde4c7c56b92f3c7a82
--- /dev/null
+++ b/matlab/estimation/smc/smc_samplers_initialization.m
@@ -0,0 +1,81 @@
+function [particles, tlogpostkernel, loglikelihood, SimulationFolder] = smc_samplers_initialization(funobj, sampler, n, Prior, SimulationFolder, nsteps)
+
+% Initialize SMC samplers by drawing initial particles in the prior distribution.
+%
+% INPUTS
+% - TargetFun [char] string specifying the name of the objective function (posterior kernel).
+% - sampler [char] name of the sampler.
+% - n [integer] scalar, number of particles.
+% - mh_bounds [double] p×2 matrix defining lower and upper bounds for the estimated parameters.
+% - dataset_ [dseries] sample
+% - dataset_info [struct] informations about the dataset
+% - options_ [struct] dynare's options
+% - M_ [struct] model description
+% - estim_params_ [struct] estimated parameters
+% - bayestopt_ [struct] estimated parameters
+% - oo_ [struct] outputs
+%
+% OUTPUTS
+% - ix2 [double] p×n matrix of particles
+% - ilogpo2 [double] n×1 vector of posterior kernel values for the particles
+% - iloglik2 [double] n×1 vector of likelihood values for the particles
+% - ModelName [string] name of the mod-file
+% - MetropolisFolder [string] path to the Metropolis subfolder
+% - bayestopt_ [structure] estimation options structure
+%
+% SPECIAL REQUIREMENTS
+% None.
+
+% Copyright © 2022-2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+dprintf('Estimation:%s: Initialization...', sampler)
+
+% Delete old mat files storign particles if any...
+matfiles = sprintf('%s%sparticles*.mat', SimulationFolder, filesep());
+files = dir(matfiles);
+if ~isempty(files)
+ delete(matfiles);
+ dprintf('Estimation:%s: Old %s-files successfully erased.', sampler, sampler)
+end
+
+% Simulate a pool of particles characterizing the prior distribution (with the additional constraint that the likelihood is finite)
+set_dynare_seed('default');
+dprintf('Estimation:%s: Searching for initial values...', sampler);
+particles = zeros(Prior.length(), n);
+tlogpostkernel = zeros(n, 1);
+loglikelihood = zeros(n, 1);
+
+t0 = tic;
+parfor j=1:n
+ notvalid = true;
+ while notvalid
+ candidate = Prior.draw();
+ if Prior.admissible(candidate)
+ particles(:,j) = candidate;
+ [tlogpostkernel(j), loglikelihood(j)] = tempered_likelihood(funobj, candidate, 0.0, Prior);
+ if isfinite(loglikelihood(j)) % if returned log-density is Inf or Nan (penalized value)
+ notvalid = false;
+ end
+ end
+ end
+end
+tt = toc(t0);
+
+save(sprintf('%s%sparticles-1-%u.mat', SimulationFolder, filesep(), nsteps), 'particles', 'tlogpostkernel', 'loglikelihood')
+dprintf('Estimation:%s: Initial values found (%.2fs)', sampler, tt)
+skipline()
diff --git a/matlab/estimation/smc/tempered_likelihood.m b/matlab/estimation/smc/tempered_likelihood.m
new file mode 100644
index 0000000000000000000000000000000000000000..bcb1e7bb661c5014474c5bf137c9a6a2d19371e8
--- /dev/null
+++ b/matlab/estimation/smc/tempered_likelihood.m
@@ -0,0 +1,35 @@
+function [tlogpostkernel,loglikelihood] = tempered_likelihood(postkernelfun, xparam, lambda, Prior)
+
+% Evaluate tempered likelihood (posterior kernel)
+%
+% INPUTS
+% - postkernelfun [handle] Function handle for the opposite of the posterior kernel.
+% - xparam [double] n×1 vector of parameters.
+% - lambda [double] scalar between 0 and 1, weight on the posterior kernel.
+% - Prior [dprior] Prior specification.
+%
+% OUTPUTS
+% - tlogpostkernel [double] scalar, value of the tempered posterior kernel.
+% - loglikelihood [double] scalar, value of the log likelihood.
+
+% Copyright © 2022-2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+logpostkernel = -postkernelfun(xparam);
+logprior = Prior.density(xparam);
+loglikelihood = logpostkernel-logprior;
+tlogpostkernel = lambda*loglikelihood + logprior;
diff --git a/matlab/fill_mh_mode.m b/matlab/fill_mh_mode.m
index 22339a22d471f72d3c6e928a020c2f5a5fce19a0..0d2581ce108a843c92e368fe0f84750114b733f1 100644
--- a/matlab/fill_mh_mode.m
+++ b/matlab/fill_mh_mode.m
@@ -1,22 +1,22 @@
-function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
-%function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
+function oo_ = fill_mh_mode(xparam1, stdh, M_, options_, estim_params_, oo_, field_name)
+
+% Fill oo_.<field_name>.mode and oo_.<field_name>.std_at_mode
%
% INPUTS
-% o xparam1 [double] (p*1) vector of estimate parameters.
-% o stdh [double] (p*1) vector of estimate parameters.
-% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
-% o estim_params_ Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
-% o options_ Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
-% o bayestopt_ Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
-% o oo_ Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
+% - xparam1 [double] p×1 vector, estimated posterior mode.
+% - stdh [double] p×1 vector, estimated posterior standard deviation.
+% - M_ [struct] Description of the model.
+% - estim_params_ [struct] Description of the estimated parameters.
+% - options_ [struct] Dynare's options.
+% - oo_ [struct] Estimation and simulation results.
%
% OUTPUTS
-% o oo_ Matlab's structure gathering the results
+% - oo_ Matlab's structure gathering the results
%
% SPECIAL REQUIREMENTS
% None.
-% Copyright © 2005-2021 Dynare Team
+% Copyright © 2005-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -42,7 +42,8 @@ np = estim_params_.np ; % Number of deep parameters.
if np
ip = nvx+nvn+ncx+ncn+1;
for i=1:np
- name = bayestopt_.name{ip};
+ k = estim_params_.param_vals(i,1);
+ name = M_.param_names{k};
oo_.([field_name '_mode']).parameters.(name) = xparam1(ip);
oo_.([field_name '_std_at_mode']).parameters.(name) = stdh(ip);
ip = ip+1;
@@ -90,4 +91,4 @@ if ncn
oo_.([field_name '_std_at_mode']).measurement_errors_corr.(name) = stdh(ip);
ip = ip+1;
end
-end
\ No newline at end of file
+end
diff --git a/matlab/issmc.m b/matlab/issmc.m
new file mode 100644
index 0000000000000000000000000000000000000000..a79d444db9239152acc6d962a0b7345b8309138d
--- /dev/null
+++ b/matlab/issmc.m
@@ -0,0 +1,20 @@
+function bool = issmc(options_)
+
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+
+bool = ishssmc(options_) || isdsmh(options_);
diff --git a/matlab/marginal_density.m b/matlab/marginal_density.m
index e9e203686446efbe86053e847b6c7627e83a0b7c..323857e8b662e83c174cb3bac9331f6ec495bd3e 100644
--- a/matlab/marginal_density.m
+++ b/matlab/marginal_density.m
@@ -60,7 +60,7 @@ xparam1 = posterior_mean;
hh = inv(SIGMA);
fprintf(' Done!\n');
if ~isfield(oo_,'posterior_mode') || (options_.mh_replic && isequal(options_.posterior_sampler_options.posterior_sampling_method,'slice'))
- oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
+ oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,oo_,'posterior');
end
% save the posterior mean and the inverse of the covariance matrix
diff --git a/matlab/mh_autocorrelation_function.m b/matlab/mh_autocorrelation_function.m
index cf10a82798c58ac95d307820db9343524dd737c2..953b542efae722a1fd5db4f8fa27e7b853111277 100644
--- a/matlab/mh_autocorrelation_function.m
+++ b/matlab/mh_autocorrelation_function.m
@@ -59,7 +59,7 @@ nblck = size(record.LastParameters,1);
clear record;
% Get all the posterior draws:
-PosteriorDraws = GetAllPosteriorDraws(M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck, blck);
+PosteriorDraws = GetAllPosteriorDraws(options_, M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck, blck);
% Compute the autocorrelation function:
[~,autocor] = sample_autocovariance(PosteriorDraws,options_.mh_autocorrelation_function_size);
diff --git a/matlab/posterior_sampler_initialization.m b/matlab/posterior_sampler_initialization.m
index a557cc6528667ae6cee22d3365f4c1d1f9a7524b..595715a2bc53dae2010d8ab4959d23f65724a577 100644
--- a/matlab/posterior_sampler_initialization.m
+++ b/matlab/posterior_sampler_initialization.m
@@ -1,8 +1,7 @@
function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfBlocks, nruns, NewFile, MAX_nruns, d, bayestopt_] = ...
- posterior_sampler_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_, dispString)
-% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfBlocks, nruns, NewFile, MAX_nruns, d, bayestopt_] = ...
-% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_, dispString)
-% Metropolis-Hastings initialization.
+ posterior_sampler_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_, dispString)
+
+% Posterior sampler initialization.
%
% INPUTS
% o TargetFun [char] string specifying the name of the objective
@@ -257,7 +256,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2 = candidate;
ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
- fprintf('%s: Initialization at the posterior mode.\n\n',dispString);
+ fprintf('%s: Initialization at the posterior mode.\n\n',dispString);
fprintf(fidlog,[' Blck ' int2str(1) 'params:\n']);
for i=1:length(ix2(1,:))
fprintf(fidlog,[' ' int2str(i) ':' num2str(ix2(1,i)) '\n']);
diff --git a/matlab/prior_bounds.m b/matlab/prior_bounds.m
index 5db18c81f43a7e5d994ebaf73cd2f6f536834fc6..78027c4c26395e77b55066eb6b99da1806fdfdfe 100644
--- a/matlab/prior_bounds.m
+++ b/matlab/prior_bounds.m
@@ -1,6 +1,5 @@
-function bounds = prior_bounds(bayestopt, priortrunc)
+function bounds = prior_bounds(bayestopt_, priortrunc)
-% function bounds = prior_bounds(bayestopt)
% computes bounds for prior density.
%
% INPUTS
@@ -31,11 +30,11 @@ if nargin<2, priortrunc = 0.0; end
assert(priortrunc>=0 && priortrunc<=1, 'Second input argument must be non negative and not larger than one.')
-pshape = bayestopt.pshape;
-p3 = bayestopt.p3;
-p4 = bayestopt.p4;
-p6 = bayestopt.p6;
-p7 = bayestopt.p7;
+pshape = bayestopt_.pshape;
+p3 = bayestopt_.p3;
+p4 = bayestopt_.p4;
+p6 = bayestopt_.p6;
+p7 = bayestopt_.p7;
bounds.lb = zeros(size(p6));
bounds.ub = zeros(size(p6));
@@ -59,12 +58,12 @@ for i=1:length(p6)
bounds.ub(i) = gaminv(1.0-priortrunc, p6(i), p7(i))+p3(i);
end
case 3
- if prior_trunc == 0
+ if priortrunc == 0
bounds.lb(i) = max(-Inf, p3(i));
bounds.ub(i) = min(Inf, p4(i));
else
- bounds.lb(i) = max(norminv(prior_trunc, p6(i), p7(i)), p3(i));
- bounds.ub(i) = min(norminv(1-prior_trunc, p6(i), p7(i)), p4(i));
+ bounds.lb(i) = max(norminv(priortrunc, p6(i), p7(i)), p3(i));
+ bounds.ub(i) = min(norminv(1-priortrunc, p6(i), p7(i)), p4(i));
end
case 4
if priortrunc==0
@@ -99,6 +98,6 @@ for i=1:length(p6)
bounds.ub(i) = p3(i)+wblinv(1.0-priortrunc, p6(i), p7(i));
end
otherwise
- error(sprintf('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i)));
+ error('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i));
end
end
diff --git a/matlab/prior_posterior_statistics.m b/matlab/prior_posterior_statistics.m
index 58f428c5a192a2e1ca26343b92f4f4a01123fa12..6971f9465660c4f7e6b624b54786b382638b83bd 100644
--- a/matlab/prior_posterior_statistics.m
+++ b/matlab/prior_posterior_statistics.m
@@ -212,9 +212,9 @@ if strcmpi(type,'posterior')
mh_nblck = options_.mh_nblck;
if B==NumberOfDraws*mh_nblck
% we load all retained MH runs !
- logpost=GetAllPosteriorDraws(M_.dname, M_.fname, 0, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
+ logpost=GetAllPosteriorDraws(options_, M_.dname, M_.fname, 0, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
for column=1:npar
- x(:,column) = GetAllPosteriorDraws(M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
+ x(:,column) = GetAllPosteriorDraws(options_, M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
end
else
logpost=NaN(B,1);
@@ -390,4 +390,4 @@ if ~isnumeric(options_.parallel)
if leaveSlaveOpen == 0
closeSlave(options_.parallel,options_.parallel_info.RemoteTmpFolder),
end
-end
\ No newline at end of file
+end
diff --git a/matlab/trace_plot.m b/matlab/trace_plot.m
index d4cf5e1f14e2e29a08dc09e5b21a8e4b54b79740..9de1860b90d218cb2020d11ec36c897f067dd4d8 100644
--- a/matlab/trace_plot.m
+++ b/matlab/trace_plot.m
@@ -67,7 +67,7 @@ n_nblocks_to_plot=length(blck);
if n_nblocks_to_plot==1
% Get all the posterior draws:
-PosteriorDraws = GetAllPosteriorDraws(M_.dname,M_.fname,column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck);
+ PosteriorDraws = GetAllPosteriorDraws(options_, M_.dname,M_.fname,column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck);
else
PosteriorDraws=NaN(TotalNumberOfMhDraws,n_nblocks_to_plot);
save_string='';
@@ -75,7 +75,7 @@ else
title_string_tex='';
end
for block_iter=1:n_nblocks_to_plot
- PosteriorDraws(:,block_iter) = GetAllPosteriorDraws(M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck(block_iter));
+ PosteriorDraws(:,block_iter) = GetAllPosteriorDraws(options_, M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck(block_iter));
save_string=[save_string,'_',num2str(blck(block_iter))];
if options_.TeX
title_string_tex=[title_string_tex, ', ' num2str(blck(block_iter))];
diff --git a/meson.build b/meson.build
index 42d7017be310461f08939f1e86209bd8c6d4dab5..bf00091ba6f161a15c84a1db4a7a358444b78568 100644
--- a/meson.build
+++ b/meson.build
@@ -805,6 +805,8 @@ mod_and_m_tests = [
'estimation/fsdat_simul.m' ] },
{ 'test' : [ 'estimation/fs2000.mod' ],
'extra' : [ 'estimation/fsdat_simul.m' ] },
+ { 'test' : [ 'estimation/hssmc/fs2000.mod' ],
+ 'extra' : [ 'estimation/fsdat_simul.m' ] },
{ 'test' : [ 'gsa/ls2003a.mod',
'gsa/ls2003.mod',
'gsa/ls2003scr.mod',
diff --git a/tests/estimation/dsmc/fs2000.mod b/tests/estimation/dsmc/fs2000.mod
new file mode 100644
index 0000000000000000000000000000000000000000..647e50f95eb78558356ec7db2f0b8c9b2aad2cf4
--- /dev/null
+++ b/tests/estimation/dsmc/fs2000.mod
@@ -0,0 +1,85 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+steady_state_model;
+ dA = exp(gam);
+ gst = 1/dA;
+ m = mst;
+ khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
+ xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
+ nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
+ n = xist/(nust+xist);
+ P = xist + nust;
+ k = khst*n;
+
+ l = psi*mst*n/( (1-psi)*(1-n) );
+ c = mst/P;
+ d = l - mst + 1;
+ y = k^alp*n^(1-alp)*gst^alp;
+ R = mst/bet;
+ W = l/n;
+ ist = y-c;
+ q = 1 - d;
+
+ e = 1;
+
+ gp_obs = m/dA;
+ gy_obs = dA;
+end;
+
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+estimation(order=1,datafile='../fsdat_simul.m',nobs=192,loglinear,posterior_sampling_method='dsmh');
diff --git a/tests/estimation/hssmc/fs2000.mod b/tests/estimation/hssmc/fs2000.mod
new file mode 100644
index 0000000000000000000000000000000000000000..d843c9e98bc434f2c30861d83e210547fd3b93d2
--- /dev/null
+++ b/tests/estimation/hssmc/fs2000.mod
@@ -0,0 +1,91 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+steady_state_model;
+ dA = exp(gam);
+ gst = 1/dA;
+ m = mst;
+ khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
+ xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
+ nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
+ n = xist/(nust+xist);
+ P = xist + nust;
+ k = khst*n;
+
+ l = psi*mst*n/( (1-psi)*(1-n) );
+ c = mst/P;
+ d = l - mst + 1;
+ y = k^alp*n^(1-alp)*gst^alp;
+ R = mst/bet;
+ W = l/n;
+ ist = y-c;
+ q = 1 - d;
+
+ e = 1;
+
+ gp_obs = m/dA;
+ gy_obs = dA;
+end;
+
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+estimation(order=1, datafile='../fsdat_simul.m', nobs=192, loglinear,
+ posterior_sampling_method='hssmc',
+ posterior_sampler_options=('steps',10,
+ 'lambda',2,
+ 'particles', 20000,
+ 'scale',.5,
+ 'target', .25));
diff --git a/tests/estimation/slice/fs2000_slice.mod b/tests/estimation/slice/fs2000_slice.mod
index f50a1ec4e08bbbb06550da2980e55e15e44dcfc2..ecaf831e3008b00c2bb22507b5fc3b4842239391 100644
--- a/tests/estimation/slice/fs2000_slice.mod
+++ b/tests/estimation/slice/fs2000_slice.mod
@@ -1,94 +1,94 @@
-// See fs2000.mod in the examples/ directory for details on the model
-
-var m P c e W R k d n l gy_obs gp_obs y dA;
-varexo e_a e_m;
-
-parameters alp bet gam mst rho psi del;
-
-alp = 0.33;
-bet = 0.99;
-gam = 0.003;
-mst = 1.011;
-rho = 0.7;
-psi = 0.787;
-del = 0.02;
-
-model;
-dA = exp(gam+e_a);
-log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
--P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
-W = l/n;
--(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
-R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
-1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
-c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
-P*c = m;
-m-1+d = l;
-e = exp(e_a);
-y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
-gy_obs = dA*y/y(-1);
-gp_obs = (P/P(-1))*m(-1)/dA;
-end;
-
-steady_state_model;
- dA = exp(gam);
- gst = 1/dA;
- m = mst;
- khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
- xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
- nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
- n = xist/(nust+xist);
- P = xist + nust;
- k = khst*n;
-
- l = psi*mst*n/( (1-psi)*(1-n) );
- c = mst/P;
- d = l - mst + 1;
- y = k^alp*n^(1-alp)*gst^alp;
- R = mst/bet;
- W = l/n;
- ist = y-c;
- q = 1 - d;
-
- e = 1;
-
- gp_obs = m/dA;
- gy_obs = dA;
-end;
-
-shocks;
-var e_a; stderr 0.014;
-var e_m; stderr 0.005;
-end;
-
-steady;
-
-check;
-
-estimated_params;
-alp, beta_pdf, 0.356, 0.02;
-bet, beta_pdf, 0.993, 0.002;
-gam, normal_pdf, 0.0085, 0.003;
-mst, normal_pdf, 1.0002, 0.007;
-rho, beta_pdf, 0.129, 0.05;
-psi, beta_pdf, 0.65, 0.05;
-del, beta_pdf, 0.01, 0.005;
-stderr e_a, inv_gamma_pdf, 0.035449, inf;
-stderr e_m, inv_gamma_pdf, 0.008862, inf;
-end;
-
-varobs gp_obs gy_obs;
-
-//options_.posterior_sampling_method = 'slice';
-estimation(order=1,datafile='../fsdat_simul',nobs=192,silent_optimizer,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
-posterior_sampling_method='slice'
-);
-// continue with rotated slice
-estimation(order=1,datafile='../fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
-posterior_sampling_method='slice',
-posterior_sampler_options=('rotated',1,'use_mh_covariance_matrix',1)
-);
-
-options_.TeX=1;
-generate_trace_plots(1:2);
-options_.TeX=1;
\ No newline at end of file
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+steady_state_model;
+ dA = exp(gam);
+ gst = 1/dA;
+ m = mst;
+ khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
+ xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
+ nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
+ n = xist/(nust+xist);
+ P = xist + nust;
+ k = khst*n;
+
+ l = psi*mst*n/( (1-psi)*(1-n) );
+ c = mst/P;
+ d = l - mst + 1;
+ y = k^alp*n^(1-alp)*gst^alp;
+ R = mst/bet;
+ W = l/n;
+ ist = y-c;
+ q = 1 - d;
+
+ e = 1;
+
+ gp_obs = m/dA;
+ gy_obs = dA;
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.05;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+//options_.posterior_sampling_method = 'slice';
+estimation(order=1,datafile='../fsdat_simul',nobs=192,silent_optimizer,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
+posterior_sampling_method='slice'
+);
+// continue with rotated slice
+estimation(order=1,datafile='../fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
+posterior_sampling_method='slice',
+posterior_sampler_options=('rotated',1,'use_mh_covariance_matrix',1)
+);
+
+options_.TeX=1;
+generate_trace_plots(1:2);
+options_.TeX=1;