dynare_v4: exploitation of posterior distribution (unfinished)
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1877 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
michel
parent
a38bec0c18
commit
dfc1f3c90f
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@ -53,6 +53,7 @@ TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
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NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
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clear record;
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B = min(1200, round(0.25*NumberOfDraws));
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B = 200;
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%%
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MAX_nruns = min(B,ceil(options_.MaxNumberOfBytes/(npar+2)/8));
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MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
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@ -0,0 +1,22 @@
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function moments=compute_model_moments(dr,options)
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% function compute_model_moments(options)
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% Computes posterior filter smoother and forecasts
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%
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% INPUTS
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% dr: structure describing model solution
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% options: structure of Dynare options
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%
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% OUTPUTS
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% moments: a cell array containing requested moments
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%
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% SPECIAL REQUIREMENTS
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% none
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%
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% part of DYNARE, copyright Dynare Team (2008)
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% Gnu Public License.
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% subset of variables
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varlist = options.varlist;
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moments = th_autocovariances(dr,varlist);
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@ -903,12 +903,9 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
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if options_.bayesian_irf
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PosteriorIRF('posterior');
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end
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if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast
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PosteriorFilterSmootherAndForecast(data,gend,'posterior');
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end
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if options_.moments_varendo
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PosteriorMomentsVarendo('posterior');
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if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast ...
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| options_.moments_varendo
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prior_posterior_statistics('posterior',data,gend);
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end
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xparam = get_posterior_parameters('mean');
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@ -0,0 +1,32 @@
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function s=get_moments_size(options)
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% function PosteriorFilterSmootherAndForecast(Y,gend, type)
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% Computes posterior filter smoother and forecasts
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%
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% INPUTS
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% options: structure of Dynare options
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%
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% OUTPUTS
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% s: size of moments for a given model and options
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%
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% SPECIAL REQUIREMENTS
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% none
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%
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% part of DYNARE, copyright Dynare Team (2008)
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% Gnu Public License.
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global M_
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n = size(options.varlist,1);
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if n == 0
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n = M_.endo_nbr;
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end
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n2 = n*n;
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s = n; % mean
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s = s + n; % std errors
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s = s + n2; % variance
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s = s + n2; % correlations
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s = s + options.ar*n2; % auto-correlations
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@ -144,7 +144,8 @@ function global_initialization()
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options_.simulation_method = 0;
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options_.cutoff = 1e-12;
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options_.student_degrees_of_freedom = 3;
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options_.subdraws = [];
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% Misc
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options_.conf_sig = 0.6;
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oo_.exo_simul = [];
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@ -0,0 +1,269 @@
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function prior_posterior_statistics(type,Y,gend)
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% function PosteriorFilterSmootherAndForecast(Y,gend, type)
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% Computes posterior filter smoother and forecasts
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%
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% INPUTS
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% type: posterior
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% prior
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% gsa
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% Y: data
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% gend: number of observations
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% none
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%
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% part of DYNARE, copyright Dynare Team (2005-2008)
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% Gnu Public License.
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global options_ estim_params_ oo_ M_ bayestopt_
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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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npar = nvx+nvn+ncx+ncn+np;
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offset = npar-np;
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naK = length(options_.filter_step_ahead);
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%%
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MaxNumberOfBytes=options_.MaxNumberOfBytes;
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endo_nbr=M_.endo_nbr;
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exo_nbr=M_.exo_nbr;
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nvobs = size(options_.varobs,1);
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iendo = 1:endo_nbr;
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horizon = options_.forecast;
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moments_varendo = options_.moments_varendo;
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if horizon
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i_last_obs = gend+(1-M_.maximum_endo_lag:0);
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end
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maxlag = M_.maximum_endo_lag;
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%%
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DirectoryName = CheckPath('metropolis');
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load([ DirectoryName '/' M_.fname '_mh_history'])
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FirstMhFile = record.KeepedDraws.FirstMhFile;
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FirstLine = record.KeepedDraws.FirstLine;
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TotalNumberOfMhFiles = sum(record.MhDraws(:,2)); LastMhFile = TotalNumberOfMhFiles;
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TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
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NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
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clear record;
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if ~isempty(options_.subdraws)
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B = options_.subdraws;
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if B > NumberOfDraws
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B = NumberOfDraws;
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end
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else
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B = min(1200, round(0.25*NumberOfDraws));
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end
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%%
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MAX_nruns = min(B,ceil(MaxNumberOfBytes/(npar+2)/8));
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MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
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MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
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MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
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if naK
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MAX_naK = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
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length(options_.filter_step_ahead)*gend)/8));
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end
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if horizon
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MAX_nforc1 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/8));
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MAX_nforc2 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/ ...
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8));
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IdObs = bayestopt_.mfys;
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end
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MAX_momentsno = min(B,ceil(MaxNumberOfBytes/(get_moments_size(options_)*8)));
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%%
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varlist = options_.varlist;
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if isempty(varlist)
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varlist = M_.endo_names;
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SelecVariables = transpose(1:M_.endo_nbr);
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nvar = M_.endo_nbr;
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else
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nvar = size(varlist,1);
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SelecVariables = [];
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for i=1:nvar
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if ~isempty(strmatch(varlist(i,:),M_.endo_names,'exact'))
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SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,'exact')];
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end
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end
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end
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irun = ones(8,1);
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ifil = zeros(8,1);
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h = waitbar(0,'Taking subdraws...');
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stock_param = zeros(MAX_nruns, npar);
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stock_logpo = zeros(MAX_nruns,1);
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stock_ys = zeros(MAX_nruns,endo_nbr);
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run_smoother = 0;
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if options_.smoother
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stock_smooth = zeros(endo_nbr,gend,MAX_nsmoo);
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stock_innov = zeros(exo_nbr,gend,B);
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stock_error = zeros(nvobs,gend,MAX_nerro);
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run_smoother = 1;
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end
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if options_.filter_step_ahead
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stock_filter = zeros(naK,endo_nbr,gend+ ...
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options_.filter_step_ahead(end),MAX_naK);
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run_smoother = 1;
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end
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if options_.forecast
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stock_forcst_mean = zeros(endo_nbr,horizon+maxlag,MAX_nforc1);
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stock_forcst_total = zeros(endo_nbr,horizon+maxlag,MAX_nforc2);
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run_smoother = 1;
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end
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if moments_varendo
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stock_moments = cell(MAX_momentsno,1);
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end
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for b=1:B
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[deep, logpo] = GetOneDraw(type);
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set_all_parameters(deep);
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dr = resol(oo_.steady_state,0);
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if moments_varendo
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stock_moments{irun(8)} = compute_model_moments(dr,options_);
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end
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if run_smoother
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[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = ...
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DsgeSmoother(deep,gend,Y);
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if options_.loglinear
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stock_smooth(dr.order_var,:,irun(1)) = alphahat(1:endo_nbr,:)+ ...
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repmat(log(dr.ys(dr.order_var)),1,gend);
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else
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stock_smooth(dr.order_var,:,irun(1)) = alphahat(1:endo_nbr,:)+ ...
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repmat(dr.ys(dr.order_var),1,gend);
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end
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if nvx
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stock_innov(:,:,irun(2)) = etahat;
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end
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if nvn
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stock_error(:,:,irun(3)) = epsilonhat;
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end
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if naK
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stock_filter(:,dr.order_var,:,irun(4)) = aK(options_.filter_step_ahead,1:endo_nbr,:);
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end
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if horizon
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yyyy = alphahat(iendo,i_last_obs);
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yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
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if options_.prefilter == 1
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yf(:,IdObs) = yf(:,IdObs)+repmat(bayestopt_.mean_varobs', ...
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horizon+maxlag,1);
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end
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yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
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if options_.loglinear == 1
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yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
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% yf = exp(yf);
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else
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yf = yf+repmat(SteadyState',horizon+maxlag,1);
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end
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yf1 = forcst2(yyyy,horizon,dr,1);
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if options_.prefilter == 1
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yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
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repmat(bayestopt_.mean_varobs',[horizon+maxlag,1,1]);
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end
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yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
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trend_coeff',[1,1,1]);
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if options_.loglinear == 1
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yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
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% yf1 = exp(yf1);
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else
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yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
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end
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stock_forcst_mean(:,:,irun(6)) = yf';
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stock_forcst_total(:,:,irun(7)) = yf1';
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end
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end
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stock_param(irun(5),:) = deep;
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stock_logpo(irun(5),1) = logpo;
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stock_ys(irun(5),:) = SteadyState';
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irun = irun + ones(8,1);
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if irun(1) > MAX_nsmoo | b == B
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stock = stock_smooth(:,:,1:irun(1)-1);
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ifil(1) = ifil(1) + 1;
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save([DirectoryName '/' M_.fname '_smooth' int2str(ifil(1))],'stock');
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irun(1) = 1;
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end
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if nvx & (irun(2) > MAX_ninno | b == B)
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stock = stock_innov(:,:,1:irun(2)-1);
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ifil(2) = ifil(2) + 1;
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save([DirectoryName '/' M_.fname '_inno' int2str(ifil(2))],'stock');
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irun(2) = 1;
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end
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if nvn & (irun(3) > MAX_error | b == B)
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stock = stock_error(:,:,1:irun(3)-1);
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ifil(3) = ifil(3) + 1;
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save([DirectoryName '/' M_.fname '_error' int2str(ifil(3))],'stock');
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irun(3) = 1;
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end
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if naK & (irun(4) > MAX_naK | b == B)
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stock = stock_filter(:,:,:,1:irun(4)-1);
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ifil(4) = ifil(4) + 1;
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save([DirectoryName '/' M_.fname '_filter' int2str(ifil(4))],'stock');
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irun(4) = 1;
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end
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if irun(5) > MAX_nruns | b == B
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stock = stock_param(1:irun(5)-1,:);
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ifil(5) = ifil(5) + 1;
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save([DirectoryName '/' M_.fname '_param' int2str(ifil(5))],'stock','stock_logpo','stock_ys');
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irun(5) = 1;
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end
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if horizon & (irun(6) > MAX_nforc1 | b == B)
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stock = stock_forcst_mean(:,:,1:irun(6)-1);
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ifil(6) = ifil(6) + 1;
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save([DirectoryName '/' M_.fname '_forc_mean' int2str(ifil(6))],'stock');
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irun(6) = 1;
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end
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if horizon & (irun(7) > MAX_nforc2 | b == B)
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stock = stock_forcst_total(:,:,1:irun(7)-1);
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ifil(7) = ifil(7) + 1;
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save([DirectoryName '/' M_.fname '_forc_total' int2str(ifil(7))],'stock');
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irun(7) = 1;
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end
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if moments_varendo & (irun(8) > MAX_momentsno | b == B)
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stock = stock_moments(1:irun(8)-1);
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ifil(8) = ifil(8) + 1;
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save([DirectoryName '/' M_.fname '_moments' int2str(ifil(8))],'stock');
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irun(8) = 1;
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end
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waitbar(b/B,h);
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end
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close(h)
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stock_gend=gend;
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stock_data=Y;
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save([DirectoryName '/' M_.fname '_data'],'stock_gend','stock_data');
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if options_.smoother
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pm3(endo_nbr,gend,ifil(1),B,'Smoothed variables',...
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M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
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'names2','smooth',[M_.fname '/metropolis'],'_smooth')
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end
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if options_.forecast
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pm3(endo_nbr,horizon+maxlag,ifil(6),B,'Forecasted variables (mean)',...
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M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
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'names2','smooth',[M_.fname '/metropolis'],'_forc_mean')
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pm3(endo_nbr,horizon+maxlag,ifil(6),B,'Forecasted variables (total)',...
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M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
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'names2','smooth',[M_.fname '/metropolis'],'_forc_total')
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end
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