diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index 6c37fd1a468cc7ac4e586075e727ffbc059950fd..f4cf0a8b2fa2e30aa2a1721760597b6ac22a6930 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model.
%
% INPUTS
@@ -55,7 +55,11 @@ end
nx = EstimatedParameters.nvx + EstimatedParameters.np;
% Get the number of observed variables in the VAR model.
-NumberOfObservedVariables = DynareDataset.info.nvobs;
+NumberOfObservedVariables = DynareDataset.vobs;
+
+% Get the number of observations.
+NumberOfObservations = DynareDataset.nobs;
+
% Get the number of lags in the VAR model.
NumberOfLags = DynareOptions.dsge_varlag;
@@ -110,8 +114,8 @@ Model.Sigma_e = Q;
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
% Is the dsge prior proper?
-if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.info.ntobs;
- fval = objective_function_penalty_base+abs(DynareDataset.info.ntobs*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
+if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/NumberOfObservations;
+ fval = objective_function_penalty_base+abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
exit_flag = 0;
info = 51;
return
@@ -197,9 +201,9 @@ assignin('base','GXX',GXX);
assignin('base','GYX',GYX);
if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
- tmp0 = dsge_prior_weight*DynareDataset.info.ntobs*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
- tmp1 = dsge_prior_weight*DynareDataset.info.ntobs*GYX + mYX;
- tmp2 = inv(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX);
+ tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
+ tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
+ tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
if ~SIGMAu_is_positive_definite
@@ -208,28 +212,28 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
exit_flag = 0;
return;
end
- SIGMAu = SIGMAu / (DynareDataset.info.ntobs*(1+dsge_prior_weight));
+ SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
PHI = tmp2*tmp1'; clear('tmp1');
- prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*DynareDataset.info.ntobs- ...
+ prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
- prodlng2 = sum(gammaln(.5*(dsge_prior_weight*DynareDataset.info.ntobs- ...
+ prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
- lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX)) ...
- + .5*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters)*log(det((dsge_prior_weight+1)*DynareDataset.info.ntobs*SIGMAu)) ...
- - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX)) ...
- - .5*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters)*log(det(dsge_prior_weight*DynareDataset.info.ntobs*(GYY-GYX*inv(GXX)*GYX'))) ...
- + .5*NumberOfObservedVariables*DynareDataset.info.ntobs*log(2*pi) ...
- - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters) ...
- + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters) ...
+ lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
+ + .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
+ - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
+ - .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
+ + .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi) ...
+ - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
+ + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
- prodlng1 + prodlng2;
else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
iGXX = inv(GXX);
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
PHI = iGXX*transpose(GYX);
- lik = DynareDataset.info.ntobs * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
- trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/DynareDataset.info.ntobs));
+ lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
+ trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
lik = .5*lik;% Minus likelihood
end
@@ -254,7 +258,7 @@ if (nargout==9)
iGXX = inv(GXX);
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
prior.PHIstar = iGXX*transpose(GYX);
- prior.ArtificialSampleSize = fix(dsge_prior_weight*DynareDataset.info.ntobs);
+ prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX = iGXX;
end
\ No newline at end of file
diff --git a/matlab/dsgevar_posterior_density.m b/matlab/dsgevar_posterior_density.m
index 16f593f0628f5caca2ea5093be81b8fe04603ef6..8e359f3ce447c6417b2055c9f1107d511b3b81a2 100644
--- a/matlab/dsgevar_posterior_density.m
+++ b/matlab/dsgevar_posterior_density.m
@@ -1,4 +1,4 @@
-function bvar = dsgevar_posterior_density(deep,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function bvar = dsgevar_posterior_density(deep,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% This function characterizes the posterior distribution of a bvar with
% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value
% of the deep parameters (structural parameters + the size of the
@@ -47,7 +47,7 @@ if ~options_.noconstant
bvar.NumberOfVariables;
end
-[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
% Matric-variate normal distribution.
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 861af4d58d9cd460ad26231cad9159519fdf1020..7dbca0d8d6d4ab68421384f4c8bdef176478a4af 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -83,6 +83,15 @@ end
if options_.dsge_var
check_dsge_var_model(M_, estim_params_, bayestopt_);
+ if dataset_info.missing.state
+ error('Estimation::DsgeVarLikelihood: I cannot estimate a DSGE-VAR model with missing observations!')
+ end
+ if options_.noconstant
+ var_sample_moments(options_.dsge_varlag, -1, dataset_);
+ else
+ % The steady state is non zero ==> a constant in the VAR is needed!
+ var_sample_moments(options_.dsge_varlag, 0, dataset_);
+ end
end
%check for calibrated covariances before updating parameters
@@ -155,25 +164,6 @@ if ~isempty(estim_params_)
M_ = set_all_parameters(xparam1,estim_params_,M_);
end
-% compute sample moments if needed (bvar-dsge)
-if options_.dsge_var
- if dataset_info.missing.state
- error('I cannot estimate a DSGE-VAR model with missing observations!')
- end
- if options_.noconstant
- evalin('base',...
- ['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
- 'var_sample_moments(options_.first_obs,' ...
- 'options_.first_obs+options_.nobs-1,options_.dsge_varlag,-1,' ...
- 'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
- else% The steady state is non zero ==> a constant in the VAR is needed!
- evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
- 'var_sample_moments(options_.first_obs,' ...
- 'options_.first_obs+options_.nobs-1,options_.dsge_varlag,0,' ...
- 'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
- end
-end
-
%% perform initial estimation checks;
try
diff --git a/matlab/utilities/dataset/makedataset.m b/matlab/utilities/dataset/makedataset.m
index d6ff48d4d01d0d65d3891081c27df8c9a8d0dd20..6af59b0652dbedcb303a655a6fe5ac2a336e350f 100644
--- a/matlab/utilities/dataset/makedataset.m
+++ b/matlab/utilities/dataset/makedataset.m
@@ -1,4 +1,4 @@
-function [DynareDataset, DatasetInfo] = makedataset(DynareOptions)
+function [DynareDataset, DatasetInfo] = makedataset(DynareOptions,initialconditions)
% Initialize a dataset as a dseries object.
%
@@ -23,6 +23,12 @@ function [DynareDataset, DatasetInfo] = makedataset(DynareOptions)
%
% See also dynare_estimation_init
+if nargin<2
+ % If a the sample is to be used for the estimation of a VAR or DSGE-VAR model
+ % the second argument must be a strictly positive integer (the number of lags).
+ initialconditions = 0;
+end
+
if isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file) && isempty(DynareOptions.dataset.series)
if gsa_flag
DynareDataset = dseries();
@@ -174,11 +180,19 @@ else
end
end
+% Add initial conditions if needed
+FIRSTOBS = firstobs-initialconditions;
+
% Check that firstobs belongs to DynareDataset.dates
if firstobs<DynareDataset.init
error(sprintf('first_obs (%s) cannot be less than the first date in the dataset (%s)!',char(firstobs),char(DynareDataset.init)))
end
+% Check that FIRSTOBS belongs to DynareDataset.dates
+if initialconditions && FIRSTOBS<DynareDataset.init
+ error(sprintf('first_obs (%s) - %i cannot be less than the first date in the dataset (%s)!\nReduce the number of lags in the VAR model or increase the value of first_obs.', char(firstobs), initialconditions, char(DynareDataset.init)));
+end
+
% Check that lastobs belongs to DynareDataset.dates...
if newdatainterface
if lastobs>DynareDataset.dates(end)
@@ -192,7 +206,7 @@ else
end
% Select a subsample.
-DynareDataset = DynareDataset(firstobs:lastobs);
+DynareDataset = DynareDataset(FIRSTOBS:lastobs);
% Initialize DatasetInfo structure.
DatasetInfo = struct('missing', struct('state', NaN, 'aindex', [], 'vindex', [], 'number_of_observations', NaN, 'no_more_missing_observations', NaN), ...
diff --git a/matlab/var_sample_moments.m b/matlab/var_sample_moments.m
index 94cd63de612de5cdaac8efaf44bf8a9e6de37cb2..f9d4f8b022f429aaf57866916a03811f96c5ac10 100644
--- a/matlab/var_sample_moments.m
+++ b/matlab/var_sample_moments.m
@@ -1,29 +1,28 @@
-function [YtY,XtY,YtX,XtX,Y,X] = ...
- var_sample_moments(FirstObservation,LastObservation,qlag,var_trend_order,datafile,varobs,xls_sheet,xls_range)
+function var_sample_moments(nlag, var_trend_order, dataset_)%datafile,varobs,xls_sheet,xls_range)
% Computes the sample moments of a VAR model.
%
% The VAR(p) model is defined by:
%
-% y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t for t = 1,...,T
+% y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t for t = 1,...,T
%
% where y_t is a 1*m vector of observed endogenous variables, p is the
% number of lags, A_k is an m*m real matrix, z_t is a 1*q vector of
% exogenous (deterministic) variables, C is a q*m real matrix and
% e_t is a vector of exogenous stochastic shocks. T is the number
-% of observations. The deterministic exogenous variables are assumed to
-% be a polynomial trend of order q = "var_trend_order".
+% of observations. The deterministic exogenous variables are assumed to
+% be a polynomial trend of order q = "var_trend_order".
%
-% We define:
+% We define:
%
% <> Y = (y_1',y_2',...,y_T')' a T*m matrix,
%
-% <> x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector,
+% <> x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector,
%
-% <> X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix,
+% <> X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix,
%
% <> E = (e_1',e_2',...,e_T')' a T*m matrix and
%
-% <> A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.
+% <> A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.
%
% So that we can equivalently write the VAR(p) model using the following
% matrix representation:
@@ -31,18 +30,17 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
% Y = X * A +E
%
%
-% INPUTS
-% o FirstObservation [integer] First observation.
-% o LastObservation [integer] Last observation.
-% o qlag [integer] Number of lags in the VAR model.
-% o var_trend_order [integer] Order of the polynomial exogenous trend:
+% INPUTS
+% o nlag [integer] Number of lags in the VAR model.
+% o var_trend_order [integer] Order of the polynomial exogenous trend:
% = -1 no constant and no linear trend,
% = 0 constant and no linear trend,
% = 1 constant and linear trend.
+% o dataset_ [dseries] The sample.
%
-% OUTPUTS
+% OUTPUTS
% o YtY [double] Y'*Y an m*m matrix.
-% o XtY [double] X'*Y an (mp+q)*m matrix.
+% o XtY [double] X'*Y an (mp+q)*m matrix.
% o YtX [double] Y'*X an m*(mp+q) matrix.
% o XtX [double] X'*X an (mp+q)*(mp+q) matrix.
% o Y [double] Y a T*m matrix.
@@ -50,8 +48,11 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
%
% SPECIAL REQUIREMENTS
% None.
+%
+% REMARKS
+% Outputs are saved in the base workspace (not returned by the function).
-% Copyright (C) 2007-2009 Dynare Team
+% Copyright (C) 2007-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -68,42 +69,35 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-X = [];
-Y = [];
-YtY = [];
-YtX = [];
-XtY = [];
-XtX = [];
-
-data = read_variables(datafile,char(varobs),[],xls_sheet,xls_range);
+LastObservation = dataset_.dates(end);
+FirstObservation = dataset_.dates(1)+nlag;
-if qlag > FirstObservation
- error('VarSampleMoments :: not enough data to initialize! Try to increase FirstObservation.')
- return
-end
+NumberOfObservations = LastObservation-FirstObservation+1;
+NumberOfVariables = dataset_.vobs;
-NumberOfObservations = LastObservation-FirstObservation+1;% This is T.
-NumberOfVariables = length(varobs);% This is m.
-if var_trend_order == -1% No constant no linear trend case.
- X = zeros(NumberOfObservations,NumberOfVariables*qlag);
-elseif var_trend_order == 0% Constant and no linear trend case.
-X = ones(NumberOfObservations,NumberOfVariables*qlag+1);
-indx = NumberOfVariables*qlag+1;
-elseif var_trend_order == 1;% Constant and linear trend case.
-X = ones(NumberOfObservations,NumberOfVariables*qlag+2);
-indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
+if isequal(var_trend_order,-1)
+ % No constant no linear trend case.
+ X = zeros(NumberOfObservations,NumberOfVariables*nlag);
+elseif isequal(var_trend_order,0)
+ % Constant and no linear trend case.
+ X = ones(NumberOfObservations,NumberOfVariables*nlag+1);
+ indx = NumberOfVariables*nlag+1;
+elseif isequal(var_trend_order,1)
+ % Constant and linear trend case.
+ X = ones(NumberOfObservations,NumberOfVariables*nlag+2);
+ indx = NumberOfVariables*nlag+1:NumberOfVariables*nlag+2;
else
- error('var_sample_moments :: trend must be equal to -1,0 or 1!')
+ error('Estimation::var_sample_moments: trend must be equal to -1,0 or 1!')
return
end
-% I build matrices Y and X
-Y = data(FirstObservation:LastObservation,:);
+% I build matrices Y and X
+Y = dataset_(FirstObservation:LastObservation).data;
for t=1:NumberOfObservations
- line = t + FirstObservation-1;
- for lag = 1:qlag
- X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
+ currentdate = FirstObservation+(t-1);
+ for lag = 1:nlag
+ X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = dataset_(currentdate-lag).data;
end
end
@@ -111,7 +105,9 @@ if (var_trend_order == 1)
X(:,end) = transpose(1:NumberOfObservations)
end
-YtY = Y'*Y;
-YtX = Y'*X;
-XtY = X'*Y;
-XtX = X'*X;
\ No newline at end of file
+assignin('base', 'mYY', Y'*Y);
+assignin('base', 'mYX', Y'*X);
+assignin('base', 'mXY', X'*Y);
+assignin('base', 'mXX', X'*X);
+assignin('base', 'Ydata', Y);
+assignin('base', 'Xdata', X);
\ No newline at end of file