v4.1: Removed globals. Removed call to generalized Cholesky. Added handling of datasets with missing observations (NaN), not yet ready. Changed the covariance matrix of the jumping distribution when the posterior mode is not computed (or used). Changed call to dynare_estimation. Removed pindx in dynare_estimation. + Cosmetic changes.
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2135 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
adjemian
parent
7a93a8cda8
commit
f51ba0bd93
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@ -1,4 +1,4 @@
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function dynare_estimation(var_list_)
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function dynare_estimation(var_list_,dname)
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% function dynare_estimation(var_list_)
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% runs the estimation of the model
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%
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@ -30,12 +30,9 @@ function dynare_estimation(var_list_)
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global M_ options_ oo_ estim_params_ bayestopt_
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%% Build var_list_
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var_list_ = check_list_of_variables(options_, M_, var_list_);
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%if isempty(var_list_)
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% return
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%else
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options_.varlist = var_list_;
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%end
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options_.varlist = var_list_;
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options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
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for i = 1:size(M_.endo_names,1)
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@ -45,24 +42,28 @@ for i = 1:size(M_.endo_names,1)
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end
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end
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%% Decide if a DSGE or DSGE-VAR has to be estimated.
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if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
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options_.bvar_dsge = 1;
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end
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%% Set the order of approximation to one (if needed).
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if options_.order > 1
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options_.order = 1;
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end
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if (~isempty(options_.unit_root_vars) || options_.diffuse_filter == 1)
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if options_.lik_init == 1
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options_.lik_init = 3;
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end
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%% Set options_.lik_init equal to 3 if diffuse filter is used.
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if (options_.diffuse_filter==1) && (options_.lik_init==1)
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options_.lik_init = 3;
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end
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%% If the data are prefiltered then there must not be constants in the
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%% measurement equation of the DSGE model or in the DSGE-VAR model.
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if options_.prefilter == 1
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options_.noconstant = 1;
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end
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%% Set options related to filtered variables.
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if options_.filtered_vars ~= 0 & isempty(options_.filter_step_ahead),
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options_.filter_step_ahead = 1;
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end
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@ -73,32 +74,39 @@ if options_.filter_step_ahead ~= 0
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options_.nk = max(options_.filter_step_ahead);
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end
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%% Add something to the parser ++>
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% The user should be able to choose another name
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% for the directory...
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M_.dname = M_.fname;
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%% Set the name of the directory where (intermediary) results will be saved.
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if nargin>1
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M_.dname = dname;
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else
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M_.dname = M_.fname;
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end
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%% Set the names of the priors.
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pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
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pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
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n_varobs = size(options_.varobs,1);
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%% Set the number of observed variables.
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n_varobs = size(options_.varobs,1);
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%% Set priors over the estimated parameters.
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if ~isempty(estim_params_)
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[xparam1,estim_params_,bayestopt_,lb,ub] = set_prior(estim_params_);
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[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
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if any(bayestopt_.pshape > 0)
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% Plot prior densities.
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if options_.mode_compute
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plot_priors
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plot_priors(bayestopt_,M_,options_)
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end
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% set prior bounds and check initial value of the parameters
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% Set prior bounds
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bounds = prior_bounds(bayestopt_);
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bounds(:,1)=max(bounds(:,1),lb);
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bounds(:,2)=min(bounds(:,2),ub);
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else
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options_.mh_replic = 0;
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% No priors are declared so Dynare will estimate the model by
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% maximum likelihood with inequality constraints for the parameters.
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options_.mh_replic = 0;% No metropolis.
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bounds(:,1) = lb;
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bounds(:,2) = ub;
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end
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% Test if initial values of the estimated parameters are all between
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% the prior lower and upper bounds.
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if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
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find(xparam1 < bounds(:,1))
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find(xparam1 > bounds(:,2))
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@ -108,7 +116,7 @@ if ~isempty(estim_params_)
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ub = bounds(:,2);
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bayestopt_.lb = lb;
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bayestopt_.ub = ub;
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else
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else% If estim_params_ is empty...
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xparam1 = [];
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bayestopt_.lb = [];
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bayestopt_.ub = [];
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@ -117,66 +125,65 @@ else
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bayestopt_.p1 = [];
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bayestopt_.p2 = [];
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bayestopt_.p3 = [];
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bayestopt_.p4 = [];
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estim_params_.nvx = 0;
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estim_params_.nvn = 0;
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estim_params_.ncx = 0;
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estim_params_.ncn = 0;
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estim_params_.np = 0;
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end
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np ;
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nx = nvx+nvn+ncx+ncn+np;
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if ~isfield(options_,'trend_coeffs')
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bayestopt_.with_trend = 0;
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else
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bayestopt_.with_trend = 1;
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bayestopt_.trend_coeff = {};
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trend_coeffs = options_.trend_coeffs;
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nt = length(trend_coeffs);
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for i=1:n_varobs
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if i > length(trend_coeffs)
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bayestopt_.trend_coeff{i} = '0';
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else
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bayestopt_.trend_coeff{i} = trend_coeffs{i};
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%% Get the number of parameters to be estimated.
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nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
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nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
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ncx = estim_params_.ncx; % Covariance of the structural innovations (number of parameters).
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ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of parameters).
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np = estim_params_.np ; % Number of deep parameters.
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nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
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%% Is there a linear trend in the measurement equation?
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if ~isfield(options_,'trend_coeffs') % No!
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bayestopt_.with_trend = 0;
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else% Yes!
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bayestopt_.with_trend = 1;
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bayestopt_.trend_coeff = {};
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trend_coeffs = options_.trend_coeffs;
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nt = length(trend_coeffs);
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for i=1:n_varobs
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if i > length(trend_coeffs)
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bayestopt_.trend_coeff{i} = '0';
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else
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bayestopt_.trend_coeff{i} = trend_coeffs{i};
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end
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end
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end
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end
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bayestopt_.penalty = 1e8; % penalty
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%% Set the "size" of penalty.
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bayestopt_.penalty = 1e8;
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%% Get informations about the variables of the model.
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dr = set_state_space([],M_);
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nstatic = dr.nstatic;
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npred = dr.npred;
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nspred = dr.nspred;
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nstatic = dr.nstatic; % Number of static variables.
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npred = dr.npred; % Number of predetermined variables.
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nspred = dr.nspred; % Number of predetermined variables in the state equation.
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%% Test if observed variables are declared.
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if isempty(options_.varobs)
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error('ESTIMATION: VAROBS is missing')
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end
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%% Setting resticted state space (observed + predetermined variables)
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k = [];
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k1 = [];
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for i=1:n_varobs
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k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
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k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
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end
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% union of observed and state variables
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% Define union of observed and state variables
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k2 = union(k',[dr.nstatic+1:dr.nstatic+dr.npred]');
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% set restrict_state to postion of observed + state variables
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% in expanded state vector
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% Set restrict_state to postion of observed + state variables in expanded state vector.
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bayestopt_.restrict_var_list = k2;
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% set mf1 to positions of observed variables in restricted state vector
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% for likelihood computation
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% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
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[junk,bayestopt_.mf1] = ismember(k,k2);
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% set mf2 to positions of observed variables in expanded state vector
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% for filtering and smoothing
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% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
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bayestopt_.mf2 = k;
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bayestopt_.mfys = k1;
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@ -188,8 +195,7 @@ k = find(aux(:,2) > npred);
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aux(k,2) = aux(k,2) + sum(k2 > nstatic+npred);
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bayestopt_.restrict_aux = aux;
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%% Initialization with unit-root variables
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%% Initialization with unit-root variables.
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if ~isempty(options_.unit_root_vars)
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n_ur = size(options_.unit_root_vars,1);
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i_ur = zeros(n_ur,1);
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@ -219,46 +225,53 @@ if ~isempty(options_.unit_root_vars)
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options_.lik_init = 3;
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end % if ~isempty(options_.unit_root_vars)
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%% Test if the data file is declared.
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if isempty(options_.datafile)
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error('ESTIMATION: datafile option is missing')
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error('ESTIMATION: datafile option is missing')
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end
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%% If jscale isn't specified for an estimated parameter, use
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%% global option options_.jscale, set to 0.2, by default
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%% If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
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k = find(isnan(bayestopt_.jscale));
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bayestopt_.jscale(k) = options_.mh_jscale;
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%% Read and demean data
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%% Load and transform data.
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rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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% Set the number of observations (nobs) and build a subsample between first_obs and nobs.
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options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
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gend = options_.nobs;
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rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
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if options_.loglinear == 1 & ~options_.logdata
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rawdata = log(rawdata);
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% Take the log of the variables if needed
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if options_.loglinear % If the model is log-linearized...
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if ~options_.logdata % and if the data are not in logs, then...
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rawdata = log(rawdata);
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end
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end
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if options_.prefilter == 1
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bayestopt_.mean_varobs = mean(rawdata,1);
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data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
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else
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data = transpose(rawdata);
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end
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% Test if the observations are real numbers.
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if ~isreal(rawdata)
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error(['There are complex values in the data. Probably a wrong' ...
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' transformation'])
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error('There are complex values in the data! Probably a wrong transformation')
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end
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% Test for missing observations.
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options_.missing_data = any(any(isnan(rawdata)));
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% Prefilter the data if needed.
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if options_.prefilter == 1
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if options_.missing_data
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bayestopt_.mean_varobs = zeros(1,n_varobs);
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[rdx,cdx] = find(~isnan(rawdata));
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for variable=1:n_varobs
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tdx = find(cdx==variable);
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bayestopt_.mean_varobs(variable) = mean(rawdata(rdx(tdx)),variable);
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end
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else
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bayestopt_.mean_varobs = mean(rawdata,1);
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rawdata = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
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end
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end
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% Transpose the dataset array.
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data = transpose(rawdata);
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% set options for recursive forecast if necessary
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options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
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if options_.nobs(1) == options_.nobs(end) % no recursive estimation
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options_ = set_default_option(options_,'mh_nblck',2);
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options_ = set_default_option(options_,'nodiagnostic',0);
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else
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options_ = set_default_option(options_,'mh_nblck',1);
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options_ = set_default_option(options_,'nodiagnostic',1);
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end
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%% Set various options.
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options_ = set_default_option(options_,'mh_nblck',2);
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options_ = set_default_option(options_,'nodiagnostic',0);
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% load mode file is necessary
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if length(options_.mode_file) > 0 && options_.posterior_mode_estimation
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@ -269,14 +282,13 @@ end
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if ~isempty(estim_params_)
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set_parameters(xparam1);
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end
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if options_.steadystate_flag% if the _steadystate.m file is provided.
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if options_.steadystate_flag% if the *_steadystate.m file is provided.
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[oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]);
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else% if the steady state file is not provided.
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[dd,info] = resol(oo_.steady_state,0);
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oo_.steady_state = dd.ys; clear('dd');
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end
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if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
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disp('No constant.')
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options_.noconstant = 1;
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else
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options_.noconstant = 0;
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@ -323,7 +335,7 @@ if options_.mode_compute == 0 & length(options_.mode_file) == 0
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end
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end
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return;
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return;
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end
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%% Estimation of the posterior mode or likelihood mode
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@ -346,16 +358,8 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
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[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
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fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend);
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end
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elseif options_.mode_compute == 2
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% asamin('set','maximum_cost_repeat',0);
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% $$$ if ~options_.bvar_dsge
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% $$$ [fval,xparam1,grad,hessian_asamin,exitflag] = ...
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% $$$ asamin('minimize','DsgeLikelihood',xparam1,lb,ub,-ones(size(xparam1)),gend,data);
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% $$$ else
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% $$$ [fval,xparam1,grad,hessian_asamin,exitflag] = ...
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% $$$ asamin('minimize','DsgeVarLikelihood',xparam1,lb,ub,-ones(size(xparam1)),gend);
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% $$$ end
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error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
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elseif options_.mode_compute == 2
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error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
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elseif options_.mode_compute == 3
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optim_options = optimset('display','iter','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
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if isfield(options_,'optim_opt')
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hh = reshape(hessian('DsgeVarLikelihood',xparam1,gend),nx,nx);
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end
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save([M_.fname '_mode.mat'],'xparam1','hh','fval');
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%eval(['save ' M_.fname '_mode xparam1 hh fval;']);
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else
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save([M_.fname '_mode.mat'],'xparam1','hh','fval');
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%eval(['save ' M_.fname '_mode xparam1 hh fval;']);
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end
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end
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save([M_.fname '_mode.mat'],'xparam1','hh');
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%eval(['save ' M_.fname '_mode xparam1 hh;']);
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end
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if options_.mode_check == 1 & options_.posterior_mode_estimation
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@ -514,13 +515,13 @@ if options_.mode_check == 1 & options_.posterior_mode_estimation
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end
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if options_.posterior_mode_estimation
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hh = generalized_cholesky(hh);
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invhess = inv(hh);
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stdh = sqrt(diag(invhess));
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%hh = generalized_cholesky(hh);
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invhess = inv(hh);
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stdh = sqrt(diag(invhess));
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else
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variances = bayestopt_.pstdev.^2;
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% invhess = 0.001*diag(variances);
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invhess = 0.01*eye(length(variances));
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variances = bayestopt_.pstdev.^2;
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invhess = 0.01*diag(variances);
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%invhess = 0.01*eye(length(variances));
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end
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@ -932,11 +933,7 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
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if options_.mh_replic
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[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_);
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end
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%%
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oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_);
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%% Results are saved (in case of an anormal exit from dynare or matlab)...
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%%save([M_.fname '_results.mat'],'oo_','M_');
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%%
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oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
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metropolis_draw(1);
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if options_.bayesian_irf
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@ -948,11 +945,8 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
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if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast
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prior_posterior_statistics('posterior',data,gend);
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end
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xparam = get_posterior_parameters('mean');
|
||||
set_all_parameters(xparam);
|
||||
|
||||
% return
|
||||
end
|
||||
|
||||
if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ...
|
||||
|
|
|
|||
|
|
@ -28,39 +28,39 @@ norm_A = max(transpose(sum(abs(A))));
|
|||
gamm = max(abs(diag(A)));
|
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delta = max([eps*norm_A;eps]);
|
||||
|
||||
for j = 1:n;
|
||||
for j = 1:n
|
||||
theta_j = 0;
|
||||
for i=1:n;
|
||||
for i=1:n
|
||||
somme = 0;
|
||||
for k=1:i-1;
|
||||
somme = somme + R(k,i)*R(k,j);
|
||||
end;
|
||||
for k=1:i-1
|
||||
somme = somme + R(k,i)*R(k,j);
|
||||
end
|
||||
R(i,j) = (A(i,j) - somme)/R(i,i);
|
||||
if (A(i,j) -somme) > theta_j;
|
||||
theta_j = A(i,j) - somme;
|
||||
end;
|
||||
if i > j;
|
||||
R(i,j) = 0;
|
||||
end;
|
||||
end;
|
||||
if (A(i,j) -somme) > theta_j
|
||||
theta_j = A(i,j) - somme;
|
||||
end
|
||||
if i > j
|
||||
R(i,j) = 0;
|
||||
end
|
||||
end
|
||||
somme = 0;
|
||||
for k=1:j-1;
|
||||
for k=1:j-1
|
||||
somme = somme + R(k,j)^2;
|
||||
end;
|
||||
end
|
||||
phi_j = A(j,j) - somme;
|
||||
if j+1 <= n;
|
||||
if j+1 <= n
|
||||
xi_j = max(abs(A((j+1):n,j)));
|
||||
else;
|
||||
else
|
||||
xi_j = abs(A(n,j));
|
||||
end;
|
||||
end
|
||||
beta_j = sqrt(max([gamm ; (xi_j/n) ; eps]));
|
||||
if all(delta >= [abs(phi_j);((theta_j^2)/(beta_j^2))]);
|
||||
if all(delta >= [abs(phi_j);((theta_j^2)/(beta_j^2))])
|
||||
E(j,j) = delta - phi_j;
|
||||
elseif all(abs(phi_j) >= [((delta^2)/(beta_j^2));delta]);
|
||||
elseif all(abs(phi_j) >= [((delta^2)/(beta_j^2));delta])
|
||||
E(j,j) = abs(phi_j) - phi_j;
|
||||
elseif all(((theta_j^2)/(beta_j^2)) >= [delta;abs(phi_j)]);
|
||||
elseif all(((theta_j^2)/(beta_j^2)) >= [delta;abs(phi_j)])
|
||||
E(j,j) = ((theta_j^2)/(beta_j^2)) - phi_j;
|
||||
end;
|
||||
end
|
||||
R(j,j) = sqrt(A(j,j) - somme + E(j,j));
|
||||
end;
|
||||
end
|
||||
AA = transpose(R)*R;
|
||||
|
|
@ -1,16 +1,17 @@
|
|||
function plot_priors
|
||||
|
||||
function plot_priors(bayestopt_,M_,options_)
|
||||
% function plot_priors
|
||||
% plots prior density
|
||||
%
|
||||
% INPUTS
|
||||
% none
|
||||
% o bayestopt_ [structure]
|
||||
% o M_ [structure]
|
||||
% o options_ [structure]
|
||||
%
|
||||
% OUTPUTS
|
||||
% none
|
||||
% None
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
% None
|
||||
|
||||
% Copyright (C) 2004-2008 Dynare Team
|
||||
%
|
||||
|
|
@ -29,8 +30,6 @@ function plot_priors
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global bayestopt_ M_ options_
|
||||
|
||||
TeX = options_.TeX;
|
||||
|
||||
figurename = 'Priors';
|
||||
|
|
|
|||
|
|
@ -1,19 +1,22 @@
|
|||
function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)
|
||||
function [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_)
|
||||
% function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)
|
||||
% sets prior distributions
|
||||
%
|
||||
% INPUTS
|
||||
% estim_params_: structure characterizing parameters to be estimated
|
||||
% o estim_params_ [structure] characterizing parameters to be estimated.
|
||||
% o M_ [structure] characterizing the model.
|
||||
% o options_ [structure]
|
||||
%
|
||||
% OUTPUTS
|
||||
% xparam1: vector of parameters to be estimated (initial values)
|
||||
% estim_params_: structure characterizing parameters to be estimated
|
||||
% bayestopt_: structure characterizing priors
|
||||
% lb: lower bound
|
||||
% ub: upper bound
|
||||
% o xparam1 [double] vector of parameters to be estimated (initial values)
|
||||
% o estim_params_ [structure] characterizing parameters to be estimated
|
||||
% o bayestopt_ [structure] characterizing priors
|
||||
% o lb [double] vector of lower bounds for the estimated parameters.
|
||||
% o ub [double] vector of upper bounds for the estimated parameters.
|
||||
% o M_ [structure] characterizing the model.
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
% None
|
||||
|
||||
% Copyright (C) 2003-2008 Dynare Team
|
||||
%
|
||||
|
|
@ -31,8 +34,6 @@ function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)
|
|||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global M_ options_
|
||||
|
||||
nvx = size(estim_params_.var_exo,1);
|
||||
nvn = size(estim_params_.var_endo,1);
|
||||
|
|
|
|||
Loading…
Reference in New Issue