Commit bc37c838 authored by MichelJuillard's avatar MichelJuillard
Browse files

Revert "more fixes for penalty objective function"

This reverts commit 3dac7f88.
parent a7a3bbef
......@@ -70,7 +70,7 @@ end
if ~options_.dsge_var
if options_.particle.status
objective_function = str2func('non_linear_dsge_likelihood_1');
objective_function = str2func('non_linear_dsge_likelihood');
if strcmpi(options_.particle.filter_algorithm, 'sis')
options_.particle.algorithm = 'sequential_importance_particle_filter';
elseif strcmpi(options_.particle.filter_algorithm, 'apf')
......@@ -291,7 +291,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
if compute_hessian,
crit = options_.newrat.tolerance.f;
newratflag = newratflag>0;
hh = reshape(mr_hessian(0,xparam1,objective_function,fval,newratflag,crit,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_), nx, nx);
hh = reshape(mr_hessian(0,xparam1,objective_function,newratflag,crit,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_), nx, nx);
end
options_.kalman_algo = kalman_algo0;
end
......
function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% Evaluates the posterior kernel of a dsge model using a non linear
% filter. Deprecated interface.
% Evaluates the posterior kernel of a dsge model using a non linear filter.
%@info:
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults}] =} non_linear_dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults})
......@@ -102,7 +101,7 @@ function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,Dynar
%! @end deftypefn
%@eod:
% Copyright (C) 2010-2015 Dynare Team
% Copyright (C) 2010-2013 Dynare Team
%
% This file is part of Dynare.
%
......@@ -122,6 +121,219 @@ function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,Dynar
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
% frederic DOT karame AT univ DASH lemans DOT fr
[fval,info,exit_flag,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults] = ...
non_linear_dsge_likelihood_1(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,...
EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
persistent init_flag
persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
% Initialization of the returned arguments.
fval = [];
ys = [];
trend_coeff = [];
exit_flag = 1;
% Issue an error if loglinear option is used.
if DynareOptions.loglinear
error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!')
end
%------------------------------------------------------------------------------
% 1. Get the structural parameters & define penalties
%------------------------------------------------------------------------------
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
if (DynareOptions.mode_compute~=1) && any(xparam1<BoundsInfo.lb)
k = find(xparam1(:) < BoundsInfo.lb);
fval = Inf;
exit_flag = 0;
info(1) = 41;
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
return
end
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
if (DynareOptions.mode_compute~=1) && any(xparam1>BoundsInfo.ub)
k = find(xparam1(:)>BoundsInfo.ub);
fval = Inf;
exit_flag = 0;
info(1) = 42;
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
return
end
Model = set_all_parameters(xparam1,EstimatedParameters,Model);
Q = Model.Sigma_e;
H = Model.H;
if ~issquare(Q) || EstimatedParameters.ncx || isfield(EstimatedParameters,'calibrated_covariances')
[Q_is_positive_definite, penalty] = ispd(Q);
if ~Q_is_positive_definite
fval = Inf;
exit_flag = 0;
info(1) = 43;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances')
correct_flag=check_consistency_covariances(Q);
if ~correct_flag
penalty = sum(Q(EstimatedParameters.calibrated_covariances.position).^2);
fval = Inf;
exit_flag = 0;
info(1) = 71;
info(2) = penalty;
return
end
end
end
if ~issquare(H) || EstimatedParameters.ncn || isfield(EstimatedParameters,'calibrated_covariances_ME')
[H_is_positive_definite, penalty] = ispd(H);
if ~H_is_positive_definite
fval = Inf;
exit_flag = 0;
info(1) = 44;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances_ME')
correct_flag=check_consistency_covariances(H);
if ~correct_flag
penalty = sum(H(EstimatedParameters.calibrated_covariances_ME.position).^2);
fval = Inf;
exit_flag = 0;
info(1) = 72;
info(2) = penalty;
return
end
end
end
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 25 || info(1) == 10 || info(1) == 7
fval = Inf;
exit_flag = 0;
info(2) = 0.1;
return
elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
fval = Inf;
exit_flag = 0;
return
end
% Define a vector of indices for the observed variables. Is this really usefull?...
BayesInfo.mf = BayesInfo.mf1;
% Get needed informations for kalman filter routines.
start = DynareOptions.presample+1;
np = size(T,1);
mf = BayesInfo.mf;
Y = transpose(DynareDataset.data);
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------
% Get decision rules and transition equations.
dr = DynareResults.dr;
% Set persistent variables (first call).
if isempty(init_flag)
mf0 = BayesInfo.mf0;
mf1 = BayesInfo.mf1;
restrict_variables_idx = BayesInfo.restrict_var_list;
observed_variables_idx = restrict_variables_idx(mf1);
state_variables_idx = restrict_variables_idx(mf0);
sample_size = size(Y,2);
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(Q);
init_flag = 1;
end
ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
ReducedForm.Q = Q;
ReducedForm.H = H;
ReducedForm.mf0 = mf0;
ReducedForm.mf1 = mf1;
% Set initial condition.
switch DynareOptions.particle.initialization
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
StateVectorMean = ReducedForm.constant(mf0);
StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
StateVectorMean = ReducedForm.constant(mf0);
old_DynareOptionsperiods = DynareOptions.periods;
DynareOptions.periods = 5000;
y_ = simult(DynareResults.steady_state, dr,Model,DynareOptions,DynareResults);
y_ = y_(state_variables_idx,2001:5000);
StateVectorVariance = cov(y_');
DynareOptions.periods = old_DynareOptionsperiods;
clear('old_DynareOptionsperiods','y_');
case 3% Initial state vector covariance is a diagonal matrix.
StateVectorMean = ReducedForm.constant(mf0);
StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
otherwise
error('Unknown initialization option!')
end
ReducedForm.StateVectorMean = StateVectorMean;
ReducedForm.StateVectorVariance = StateVectorVariance;
%------------------------------------------------------------------------------
% 4. Likelihood evaluation
%------------------------------------------------------------------------------
DynareOptions.warning_for_steadystate = 0;
[s1,s2] = get_dynare_random_generator_state();
LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,start,DynareOptions.particle,DynareOptions.threads);
set_dynare_random_generator_state(s1,s2);
if imag(LIK)
info(1) = 46;
info(2) = 0.1;
likelihood = Inf;
exit_flag = 0;
elseif isnan(LIK)
info(1) = 45;
info(2) = 0.1;
likelihood = Inf;
exit_flag = 0;
else
likelihood = LIK;
end
DynareOptions.warning_for_steadystate = 1;
% ------------------------------------------------------------------------------
% Adds prior if necessary
% ------------------------------------------------------------------------------
lnprior = priordens(xparam1(:),BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
fval = (likelihood-lnprior);
if isnan(fval)
info(1) = 47;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if imag(fval)~=0
info(1) = 48;
info(2) = 0.1
fval = Inf;
exit_flag = 0;
return
end
......@@ -93,7 +93,7 @@ end
% stailstr=[' P' num2str(i) stailstr];
%end
[f0,cost_flag,arg1] = penalty_objective_function(x0,fcn,penalty,varargin{:});
[f0,junk1,junk2,cost_flag] = penalty_objective_function(x0,fcn,penalty,varargin{:});
if ~cost_flag
disp_verbose('Bad initial parameter.',Verbose)
......@@ -105,7 +105,7 @@ if NumGrad
elseif ischar(grad)
[g,badg] = grad(x0,varargin{:});
else
g=arg1;
g=junk1;
badg=0;
end
retcode3=101;
......@@ -144,7 +144,7 @@ while ~done
elseif ischar(grad),
[g1, badg1] = grad(x1,varargin{:});
else
[junk1,cost_flag,g1] = penalty_objective_function(x1,fcn,penalty,varargin{:});
[junk1,g1,junk2, cost_flag] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badg1 = ~cost_flag;
end
wall1=badg1;
......@@ -171,7 +171,7 @@ while ~done
elseif ischar(grad),
[g2, badg2] = grad(x2,varargin{:});
else
[junk1,cost_flag,g2] = penalty_objective_function(x1,fcn,penalty,varargin{:});
[junk1,g2,junk2, cost_flag] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badg2 = ~cost_flag;
end
wall2=badg2;
......@@ -203,7 +203,7 @@ while ~done
elseif ischar(grad),
[g3, badg3] = grad(x3,varargin{:});
else
[junk1,cost_flag,g3] = penalty_objective_function(x1,fcn,penalty,varargin{:});
[junk1,g3,junk2, cost_flag] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badg3 = ~cost_flag;
end
wall3=badg3;
......@@ -263,7 +263,7 @@ while ~done
elseif ischar(grad),
[gh, badgh] = grad(xh,varargin{:});
else
[junk1,cost_flag,gh] = penalty_objective_function(x1,penalty,varargin{:});
[junk1,gh,junk2, cost_flag] = penalty_objective_function(x1,penalty,varargin{:});
badgh = ~cost_flag;
end
end
......
......@@ -117,7 +117,7 @@ options_.debug=1;
%%default
options_.lik_init=1;
estimation(kalman_algo=0,mode_compute=4,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
//estimation(kalman_algo=0,mode_compute=4,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
//fval_algo_0=oo_.likelihood_at_initial_parameters;
%%Multivariate Kalman Filter
options_.lik_init=1;
......
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