Provisions for moment calibration.
Sensitivity analysis for irf and moment calibration, with new function /gsa/map_calibration.m Added new tests/gsa/ls2003a.mod for testsuite of irf/moment calibration To be done: - extend SA of calibration to MC and posterior samples (currently uses prior sample only) - documentaion - partial progress to close #267time-shift
Marco Ratto
parent
07d321c159
commit
62b4cfe631
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@ -36,6 +36,10 @@ x0=[];
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options_gsa = set_default_option(options_gsa,'datafile',[]);
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options_gsa = set_default_option(options_gsa,'rmse',0);
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options_gsa = set_default_option(options_gsa,'useautocorr',0);
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options_gsa = set_default_option(options_gsa,'moment_calibration',options_.endogenous_prior_restrictions.moment);
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options_gsa = set_default_option(options_gsa,'irf_calibration',options_.endogenous_prior_restrictions.irf);
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if isfield(options_gsa,'nograph'),
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options_.nograph=options_gsa.nograph;
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end
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@ -100,7 +104,6 @@ if options_gsa.identification,
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options_gsa = set_default_option(options_gsa,'trans_ident',0);
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options_gsa = set_default_option(options_gsa,'load_ident_files',0);
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options_gsa = set_default_option(options_gsa,'ar',1);
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options_gsa = set_default_option(options_gsa,'useautocorr',0);
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options_.ar = options_gsa.ar;
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if options_gsa.morris==0,
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disp('The option morris = 0 is no longer supported (Type I errors)')
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@ -239,6 +242,10 @@ end
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% redform_map(namendo, namlagendo, namexo, icomp, pprior, ilog, threshold)
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options_.opt_gsa = options_gsa;
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if ~isempty(options_gsa.moment_calibration) || ~isempty(options_gsa.irf_calibration),
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map_calibration(OutputDirectoryName, M_, options_, oo_, estim_params_);
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end
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if options_gsa.identification,
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map_ident_(OutputDirectoryName,options_gsa);
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end
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@ -1,8 +1,8 @@
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function info = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
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% Check for prior (sign) restrictions on irf's
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function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
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% Check for prior (sign) restrictions on irf's and theoretical moments
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%
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% INPUTS
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% T [double] n*n state space matrix
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% T [double] n*n state space matrix
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% R [double] n*k matrix of shocks
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% Model [structure]
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% DynareOptions [structure]
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@ -11,6 +11,9 @@ function info = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResu
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% OUTPUTS
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% info [double] check if prior restrictions are matched by the
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% model and related info
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% info_irf [double] array of test checks for all individual irf restrictions
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% info_moment [double] array of test checks for all individual moment restrictions
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%
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% Copyright (C) 2013 Dynare Team
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%
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@ -29,45 +32,136 @@ function info = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResu
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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endo_prior_restrictions= DynareOptions.endogenous_prior_restrictions;
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info=0;
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if isempty(endo_prior_restrictions.irf),
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% no restriction to be checked
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return
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end
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if DynareOptions.order>1,
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error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
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return
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end
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infos=[0 0];
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varlist=Model.endo_names(DynareResults.dr.order_var,:);
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varlist=varlist(DynareResults.dr.restrict_var_list,:);
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NT=1;
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for j=1:size(endo_prior_restrictions.irf,1),
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NT=max(NT,endo_prior_restrictions.irf{j,3});
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end
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for t=1:NT,
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RR = T^(t-1)*R;
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for j=1:size(endo_prior_restrictions.irf,1),
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if endo_prior_restrictions.irf{j,3}~=t,
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continue,
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end
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iendo=strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact');
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iexo=strmatch(endo_prior_restrictions.irf{j,2},Model.exo_names,'exact');
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if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2)),
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infos(j,:)=[0, 0];
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else
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if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1),
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delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
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else
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delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
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end
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infos(j,:)=[49, delt];
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info=[0 0];
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info_irf=[];
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info_moment=[];
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data_irf=[];
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data_moment=[];
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endo_prior_restrictions.irf= DynareOptions.endogenous_prior_restrictions.irf;
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endo_prior_restrictions.moment= DynareOptions.endogenous_prior_restrictions.moment;
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if ~isempty(endo_prior_restrictions.irf),
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data_irf=cell(size(endo_prior_restrictions.irf,1),1);
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if DynareOptions.order>1,
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error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
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return
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end
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varlist=Model.endo_names(DynareResults.dr.order_var,:);
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if isempty(T),
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[T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
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else % check if T and R are given in the restricted form!!!
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if size(T,1)<size(varlist,1),
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varlist=varlist(DynareResults.dr.restrict_var_list,:);
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end
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end
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NT=1;
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for j=1:size(endo_prior_restrictions.irf,1),
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NT=max(NT,max(endo_prior_restrictions.irf{j,3}));
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end
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info_irf=ones(size(endo_prior_restrictions.irf,1),2);
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for t=1:NT,
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if ~DynareOptions.relative_irf,
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RR = T^(t-1)*R*diag(sqrt(diag(Model.Sigma_e)));
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else
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RR = T^(t-1)*R*100;
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end
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for j=1:size(endo_prior_restrictions.irf,1),
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if endo_prior_restrictions.irf{j,3}~=t,
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continue,
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end
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iendo=strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact');
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iexo=strmatch(endo_prior_restrictions.irf{j,2},Model.exo_names,'exact');
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data_irf{j}=[data_irf{j}; [t RR(iendo,iexo)]];
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if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2)),
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info_irf(j,:)=info_irf(j,:).*[0, 0];
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else
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if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1),
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delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
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else
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delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
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end
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info_irf(j,:)=info_irf(j,:).*[49, delt];
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end
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end
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end
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if any(info_irf),
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info=[49,sum(info_irf(:,2))];
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end
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end
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if any(infos),
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info=[49,sum(infos(:,2))];
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if ~isempty(endo_prior_restrictions.moment),
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if DynareOptions.order>1,
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error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
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return
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end
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data_moment=cell(size(endo_prior_restrictions.moment,1),1);
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var_list_=endo_prior_restrictions.moment{1,1};
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for j=1:size(endo_prior_restrictions.moment,1),
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tmp=endo_prior_restrictions.moment{j,1};
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if ~ismember(tmp,cellstr(var_list_)),
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var_list_ = char(var_list_, tmp);
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end
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tmp=endo_prior_restrictions.moment{j,2};
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if ~ismember(tmp,cellstr(var_list_)),
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var_list_ = char(var_list_, tmp);
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end
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end
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NTmax=0;
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NTmin=0;
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for j=1:size(endo_prior_restrictions.moment,1),
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NTmax=max(NTmax,max(endo_prior_restrictions.moment{j,3}));
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NTmin=min(NTmin,min(endo_prior_restrictions.moment{j,3}));
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end
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info_moment=ones(size(endo_prior_restrictions.moment,1),2);
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nvar = size(var_list_,1);
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ivar=zeros(nvar,1);
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for i=1:nvar
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i_tmp = strmatch(var_list_(i,:),Model.endo_names,'exact');
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if isempty(i_tmp)
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error (['One of the variable specified does not exist']) ;
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else
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ivar(i) = i_tmp;
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end
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end
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DynareOptions.ar = max(abs(NTmin),NTmax);
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[gamma_y,stationary_vars] = th_autocovariances(DynareResults.dr, ivar, Model, DynareOptions,1);
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for t=NTmin:NTmax,
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RR = gamma_y{abs(t)+1};
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if t==0,
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RR = RR./(sqrt(diag(RR))*sqrt(diag(RR))')-eye(nvar)+diag(diag(gamma_y{t+1})); % becomes correlation
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end
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for j=1:size(endo_prior_restrictions.moment,1),
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if endo_prior_restrictions.moment{j,3}~=t,
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continue,
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end
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iendo1 = strmatch(endo_prior_restrictions.moment{j,1},var_list_,'exact');
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iendo2 = strmatch(endo_prior_restrictions.moment{j,2},var_list_,'exact');
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if t>0
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tmp0 = iendo1;
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iendo1=iendo2;
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iendo2=tmp0;
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end
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data_moment{j}=[data_moment{j}; [t RR(iendo1,iendo2)]];
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if (RR(iendo1,iendo2)>endo_prior_restrictions.moment{j,4}(1)) && (RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(2)),
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info_moment(j,:)=info_moment(j,:).*[0, 0];
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else
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if RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(1),
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delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(1))^2;
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else
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delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(2))^2;
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end
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info_moment(j,:)=info_moment(j,:).*[49, delt];
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end
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end
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end
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if any(info_moment),
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info=[49, info(2) + sum(info_moment(:,2))];
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end
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end
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return
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@ -609,6 +609,7 @@ options_.risky_steadystate = 0;
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% endogenous prior
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options_.endogenous_prior = 0;
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options_.endogenous_prior_restrictions.irf={};
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options_.endogenous_prior_restrictions.moment={};
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% OSR Optimal Simple Rules
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options_.osr.tolf=1e-7;
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@ -0,0 +1,168 @@
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function map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResults, EstimatedParameters)
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% Copyright (C) 2014 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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fname_ = Model.fname;
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pnames = Model.param_names(EstimatedParameters.param_vals(:,1),:);
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pvalue_ks = DynareOptions.opt_gsa.pvalue_ks;
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indx_irf = [];
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indx_moment = [];
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skipline()
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disp('Sensitivity analysis for calibration criteria')
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filetoload=[OutputDirectoryName '/' fname_ '_prior'];
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load(filetoload,'lpmat','lpmat0','istable','iunstable','iindeterm','iwrong' ,'infox')
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if ~isempty(lpmat0),
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lpmatx=lpmat0(istable,:);
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else
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lpmatx=[];
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end
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[Nsam, np] = size(lpmat);
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nshock = size(lpmat0,2);
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npT = np+nshock;
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nbr_irf_restrictions = size(DynareOptions.endogenous_prior_restrictions.irf,1);
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mat_irf=cell(nbr_irf_restrictions,1);
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for ij=1:nbr_irf_restrictions,
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mat_irf{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.irf{ij,3}));
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end
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nbr_moment_restrictions = size(DynareOptions.endogenous_prior_restrictions.moment,1);
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mat_moment=cell(nbr_moment_restrictions,1);
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for ij=1:nbr_moment_restrictions,
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mat_moment{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.moment{ij,3}));
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end
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irestrictions = [1:Nsam];
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for j=1:Nsam,
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Model = set_all_parameters([lpmat0(j,:) lpmat(j,:)]',EstimatedParameters,Model);
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[Tt,Rr,SteadyState,info] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
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if info(1)==0,
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[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,Model,DynareOptions,DynareResults);
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if ~isempty(info_irf)
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for ij=1:nbr_irf_restrictions,
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mat_irf{ij}(j,:)=data_irf{ij}(:,2)';
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end
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indx_irf(j,:)=info_irf(:,1);
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end
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if ~isempty(info_moment)
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for ij=1:nbr_moment_restrictions,
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mat_moment{ij}(j,:)=data_moment{ij}(:,2)';
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end
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indx_moment(j,:)=info_moment(:,1);
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end
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else
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irestrictions(j)=0;
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end
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end
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irestrictions=irestrictions(find(irestrictions));
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xmat=[lpmat0(irestrictions,:) lpmat(irestrictions,:)];
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skipline()
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if ~isempty(indx_irf),
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indx_irf = indx_irf(irestrictions,:);
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h1=dyn_figure(DynareOptions,'name','Evaluation of irf restrictions');
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nrow=ceil(sqrt(nbr_irf_restrictions));
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ncol=nrow;
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if nrow*(nrow-1)>nbr_irf_restrictions,
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ncol=nrow-1;
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end
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for ij=1:nbr_irf_restrictions,
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figure(h1),
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mat_irf{ij}=mat_irf{ij}(irestrictions,:);
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subplot(nrow,ncol,ij),
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for ik=1:size(mat_irf{ij},2),
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cumplot(mat_irf{ij}(:,ik)),
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hold all,
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end
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% hist(mat_irf{ij}),
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a=axis;
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x1val=max(DynareOptions.endogenous_prior_restrictions.irf{ij,4}(1),a(1));
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x2val=min(DynareOptions.endogenous_prior_restrictions.irf{ij,4}(2),a(2));
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hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'k');
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set(hp,'FaceAlpha',[0.5])
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hold off,
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leg = num2str(DynareOptions.endogenous_prior_restrictions.irf{ij,3}(1));
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if size(mat_irf{ij},2)>1,
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leg = [leg,':' ,num2str(DynareOptions.endogenous_prior_restrictions.irf{ij,3}(end))];
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end
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title([DynareOptions.endogenous_prior_restrictions.irf{ij,1},' vs ',DynareOptions.endogenous_prior_restrictions.irf{ij,2}, '(', leg,')'],'interpreter','none'),
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indx1 = find(indx_irf(:,ij)==0);
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indx2 = find(indx_irf(:,ij)~=0);
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atitle=[DynareOptions.endogenous_prior_restrictions.irf{ij,1},' vs ',DynareOptions.endogenous_prior_restrictions.irf{ij,2}, '(', leg,')'];
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fprintf(['%4.1f%% of the prior support matches IRF ',atitle,' inside [%4.1f, %4.1f]\n'],length(indx1)/length(irestrictions)*100,DynareOptions.endogenous_prior_restrictions.irf{ij,4})
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aname=['irf_calib_',int2str(ij)];
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atitle=['IRF Calib: Parameter(s) driving ',DynareOptions.endogenous_prior_restrictions.irf{ij,1},' vs ',DynareOptions.endogenous_prior_restrictions.irf{ij,2}, '(', leg,')'];
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[proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
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indplot=find(proba<pvalue_ks);
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if ~isempty(indplot)
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stab_map_1(xmat, indx1, indx2, aname, 1, indplot, OutputDirectoryName,[],atitle);
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end
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end
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dyn_saveas(h1,[OutputDirectoryName,filesep,fname_,'_irf_restrictions'],DynareOptions);
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skipline()
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end
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if ~isempty(indx_moment)
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indx_moment = indx_moment(irestrictions,:);
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h2=dyn_figure(DynareOptions,'name','Evaluation of moment restrictions');
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nrow=ceil(sqrt(nbr_moment_restrictions));
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ncol=nrow;
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if nrow*(nrow-1)>nbr_moment_restrictions,
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||||
ncol=nrow-1;
|
||||
end
|
||||
for ij=1:nbr_moment_restrictions,
|
||||
figure(h2),
|
||||
mat_moment{ij}=mat_moment{ij}(irestrictions,:);
|
||||
subplot(nrow,ncol,ij),
|
||||
for ik=1:size(mat_moment{ij},2),
|
||||
cumplot(mat_moment{ij}(:,ik)),
|
||||
hold all,
|
||||
end
|
||||
% hist(mat_moment{ij}),
|
||||
a=axis;
|
||||
x1val=max(DynareOptions.endogenous_prior_restrictions.moment{ij,4}(1),a(1));
|
||||
x2val=min(DynareOptions.endogenous_prior_restrictions.moment{ij,4}(2),a(2));
|
||||
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'k');
|
||||
set(hp,'FaceAlpha',[0.5])
|
||||
hold off,
|
||||
leg = num2str(DynareOptions.endogenous_prior_restrictions.moment{ij,3}(1));
|
||||
if size(mat_moment{ij},2)>1,
|
||||
leg = [leg,':' ,num2str(DynareOptions.endogenous_prior_restrictions.moment{ij,3}(end))];
|
||||
end
|
||||
title([DynareOptions.endogenous_prior_restrictions.moment{ij,1},' vs ',DynareOptions.endogenous_prior_restrictions.moment{ij,2},'(',leg,')'],'interpreter','none'),
|
||||
|
||||
indx1 = find(indx_moment(:,ij)==0);
|
||||
indx2 = find(indx_moment(:,ij)~=0);
|
||||
atitle=[DynareOptions.endogenous_prior_restrictions.moment{ij,1},' vs ',DynareOptions.endogenous_prior_restrictions.moment{ij,2}, '(', leg,')'];
|
||||
fprintf(['%4.1f%% of the prior support matches MOMENT ',atitle,' inside [%4.1f, %4.1f]\n'],length(indx1)/length(irestrictions)*100,DynareOptions.endogenous_prior_restrictions.moment{ij,4})
|
||||
aname=['moment_calib_',int2str(ij)];
|
||||
atitle=['MOMENT Calib: Parameter(s) driving ',DynareOptions.endogenous_prior_restrictions.moment{ij,1},' vs ',DynareOptions.endogenous_prior_restrictions.moment{ij,2}, '(', leg,')'];
|
||||
[proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
|
||||
indplot=find(proba<pvalue_ks);
|
||||
if ~isempty(indplot)
|
||||
stab_map_1(xmat, indx1, indx2, aname, 1, indplot, OutputDirectoryName,[],atitle);
|
||||
end
|
||||
end
|
||||
dyn_saveas(h2,[OutputDirectoryName,filesep,fname_,'_moment_restrictions'],DynareOptions);
|
||||
skipline()
|
||||
end
|
||||
return
|
||||
|
||||
|
|
@ -253,7 +253,7 @@ if fload==0,
|
|||
iindeterm=zeros(1,Nsam);
|
||||
iwrong=zeros(1,Nsam);
|
||||
for j=1:Nsam,
|
||||
M_.params(estim_params_.param_vals(:,1)) = lpmat(j,:)';
|
||||
M_ = set_all_parameters([lpmat0(j,:) lpmat(j,:)]',estim_params_,M_);
|
||||
%try stoch_simul([]);
|
||||
try
|
||||
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
|
||||
|
|
@ -496,7 +496,7 @@ delete([OutputDirectoryName,filesep,fname_,'_',auname,'_corr_*.*']);
|
|||
delete([OutputDirectoryName,filesep,fname_,'_',aunstname,'_corr_*.*']);
|
||||
delete([OutputDirectoryName,filesep,fname_,'_',aindname,'_corr_*.*']);
|
||||
|
||||
if length(iunstable)>0 && length(iunstable)<Nsam,
|
||||
if length(iunstable)>0 ,
|
||||
fprintf(['%4.1f%% of the prior support gives unique saddle-path solution.\n'],length(istable)/Nsam*100)
|
||||
fprintf(['%4.1f%% of the prior support gives explosive dynamics.\n'],(length(iunstable)-length(iwrong)-length(iindeterm) )/Nsam*100)
|
||||
if ~isempty(iindeterm),
|
||||
|
|
@ -504,137 +504,137 @@ if length(iunstable)>0 && length(iunstable)<Nsam,
|
|||
end
|
||||
if ~isempty(iwrong),
|
||||
skipline()
|
||||
disp(['For ',num2str(length(iwrong)/Nsam*100,'%1.3f'),'\% of the prior support dynare could not find a solution.'])
|
||||
disp(['For ',num2str(length(iwrong)/Nsam*100,'%4.1f'),'% of the prior support dynare could not find a solution.'])
|
||||
skipline()
|
||||
if any(infox==1),
|
||||
disp([' For ',num2str(length(find(infox==1))/Nsam*100,'%1.3f'),'\% The model doesn''t determine the current variables uniquely.'])
|
||||
disp([' For ',num2str(length(find(infox==1))/Nsam*100,'%4.1f'),'% The model doesn''t determine the current variables uniquely.'])
|
||||
end
|
||||
if any(infox==2),
|
||||
disp([' For ',num2str(length(find(infox==2))/Nsam*100,'%1.3f'),'\% MJDGGES returned an error code.'])
|
||||
disp([' For ',num2str(length(find(infox==2))/Nsam*100,'%4.1f'),'% MJDGGES returned an error code.'])
|
||||
end
|
||||
if any(infox==6),
|
||||
disp([' For ',num2str(length(find(infox==6))/Nsam*100,'%1.3f'),'\% The jacobian evaluated at the deterministic steady state is complex.'])
|
||||
disp([' For ',num2str(length(find(infox==6))/Nsam*100,'%4.1f'),'% The jacobian evaluated at the deterministic steady state is complex.'])
|
||||
end
|
||||
if any(infox==19),
|
||||
disp([' For ',num2str(length(find(infox==19))/Nsam*100,'%1.3f'),'\% The steadystate routine thrown an exception (inconsistent deep parameters).'])
|
||||
disp([' For ',num2str(length(find(infox==19))/Nsam*100,'%4.1f'),'% The steadystate routine thrown an exception (inconsistent deep parameters).'])
|
||||
end
|
||||
if any(infox==20),
|
||||
disp([' For ',num2str(length(find(infox==20))/Nsam*100,'%1.3f'),'\% Cannot find the steady state.'])
|
||||
disp([' For ',num2str(length(find(infox==20))/Nsam*100,'%4.1f'),'% Cannot find the steady state.'])
|
||||
end
|
||||
if any(infox==21),
|
||||
disp([' For ',num2str(length(find(infox==21))/Nsam*100,'%1.3f'),'\% The steady state is complex.'])
|
||||
disp([' For ',num2str(length(find(infox==21))/Nsam*100,'%4.1f'),'% The steady state is complex.'])
|
||||
end
|
||||
if any(infox==22),
|
||||
disp([' For ',num2str(length(find(infox==22))/Nsam*100,'%1.3f'),'\% The steady has NaNs.'])
|
||||
disp([' For ',num2str(length(find(infox==22))/Nsam*100,'%4.1f'),'% The steady has NaNs.'])
|
||||
end
|
||||
if any(infox==23),
|
||||
disp([' For ',num2str(length(find(infox==23))/Nsam*100,'%1.3f'),'\% M_.params has been updated in the steadystate routine and has complex valued scalars.'])
|
||||
disp([' For ',num2str(length(find(infox==23))/Nsam*100,'%4.1f'),'% M_.params has been updated in the steadystate routine and has complex valued scalars.'])
|
||||
end
|
||||
if any(infox==24),
|
||||
disp([' For ',num2str(length(find(infox==24))/Nsam*100,'%1.3f'),'\% M_.params has been updated in the steadystate routine and has some NaNs.'])
|
||||
disp([' For ',num2str(length(find(infox==24))/Nsam*100,'%4.1f'),'% M_.params has been updated in the steadystate routine and has some NaNs.'])
|
||||
end
|
||||
if any(infox==30),
|
||||
disp([' For ',num2str(length(find(infox==30))/Nsam*100,'%1.3f'),'\% Ergodic variance can''t be computed.'])
|
||||
disp([' For ',num2str(length(find(infox==30))/Nsam*100,'%4.1f'),'% Ergodic variance can''t be computed.'])
|
||||
end
|
||||
if any(infox==49),
|
||||
disp([' For ',num2str(length(find(infox==49))/Nsam*100,'%1.3f'),'\% The model violates one (many) endogenous prior restriction(s).'])
|
||||
disp([' For ',num2str(length(find(infox==49))/Nsam*100,'%4.1f'),'% The model violates one (many) endogenous prior restriction(s).'])
|
||||
end
|
||||
|
||||
end
|
||||
skipline()
|
||||
% Blanchard Kahn
|
||||
[proba, dproba] = stab_map_1(lpmat, istable, iunstable, aname,0);
|
||||
% indstab=find(dproba>ksstat);
|
||||
indstab=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving acceptable behaviour')
|
||||
for j=1:length(indstab),
|
||||
disp([M_.param_names(estim_params_.param_vals(indstab(j),1),:),' d-stat = ', num2str(dproba(indstab(j)),'%1.3f'),' p-value = ', num2str(proba(indstab(j)),'%1.3f')])
|
||||
end
|
||||
skipline()
|
||||
if ~isempty(indstab)
|
||||
stab_map_1(lpmat, istable, iunstable, aname, 1, indstab, OutputDirectoryName,[],atitle);
|
||||
end
|
||||
ixun=iunstable(find(~ismember(iunstable,[iindeterm,iwrong])));
|
||||
if ~isempty(iindeterm),
|
||||
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iindeterm, aindetname ,0);
|
||||
% indindet=find(dproba>ksstat);
|
||||
indindet=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving indeterminacy')
|
||||
for j=1:length(indindet),
|
||||
disp([M_.param_names(estim_params_.param_vals(indindet(j),1),:),' d-stat = ', num2str(dproba(indindet(j)),'%1.3f'),' p-value = ', num2str(proba(indindet(j)),'%1.3f')])
|
||||
if length(iunstable)<Nsam
|
||||
% Blanchard Kahn
|
||||
[proba, dproba] = stab_map_1(lpmat, istable, iunstable, aname,0);
|
||||
% indstab=find(dproba>ksstat);
|
||||
indstab=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving acceptable behaviour')
|
||||
for j=1:length(indstab),
|
||||
disp([M_.param_names(estim_params_.param_vals(indstab(j),1),:),' d-stat = ', num2str(dproba(indstab(j)),'%1.3f'),' p-value = ', num2str(proba(indstab(j)),'%1.3f')])
|
||||
end
|
||||
skipline()
|
||||
if ~isempty(indindet)
|
||||
stab_map_1(lpmat, [1:Nsam], iindeterm, aindetname, 1, indindet, OutputDirectoryName,[],aindettitle);
|
||||
if ~isempty(indstab)
|
||||
stab_map_1(lpmat, istable, iunstable, aname, 1, indstab, OutputDirectoryName,[],atitle);
|
||||
end
|
||||
end
|
||||
|
||||
if ~isempty(ixun),
|
||||
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], ixun, aunstablename,0);
|
||||
% indunst=find(dproba>ksstat);
|
||||
indunst=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving instability')
|
||||
for j=1:length(indunst),
|
||||
disp([M_.param_names(estim_params_.param_vals(indunst(j),1),:),' d-stat = ', num2str(dproba(indunst(j)),'%1.3f'),' p-value = ', num2str(proba(indunst(j)),'%1.3f')])
|
||||
ixun=iunstable(find(~ismember(iunstable,[iindeterm,iwrong])));
|
||||
if ~isempty(iindeterm),
|
||||
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iindeterm, aindetname ,0);
|
||||
% indindet=find(dproba>ksstat);
|
||||
indindet=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving indeterminacy')
|
||||
for j=1:length(indindet),
|
||||
disp([M_.param_names(estim_params_.param_vals(indindet(j),1),:),' d-stat = ', num2str(dproba(indindet(j)),'%1.3f'),' p-value = ', num2str(proba(indindet(j)),'%1.3f')])
|
||||
end
|
||||
skipline()
|
||||
if ~isempty(indindet)
|
||||
stab_map_1(lpmat, [1:Nsam], iindeterm, aindetname, 1, indindet, OutputDirectoryName,[],aindettitle);
|
||||
end
|
||||
end
|
||||
|
||||
if ~isempty(ixun),
|
||||
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], ixun, aunstablename,0);
|
||||
% indunst=find(dproba>ksstat);
|
||||
indunst=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving instability')
|
||||
for j=1:length(indunst),
|
||||
disp([M_.param_names(estim_params_.param_vals(indunst(j),1),:),' d-stat = ', num2str(dproba(indunst(j)),'%1.3f'),' p-value = ', num2str(proba(indunst(j)),'%1.3f')])
|
||||
end
|
||||
skipline()
|
||||
if ~isempty(indunst)
|
||||
stab_map_1(lpmat, [1:Nsam], ixun, aunstablename, 1, indunst, OutputDirectoryName,[],aunstabletitle);
|
||||
end
|
||||
end
|
||||
|
||||
if ~isempty(iwrong),
|
||||
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iwrong, awronguniname,0);
|
||||
% indwrong=find(dproba>ksstat);
|
||||
indwrong=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving no solution')
|
||||
for j=1:length(indwrong),
|
||||
disp([M_.param_names(estim_params_.param_vals(indwrong(j),1),:),' d-stat = ', num2str(dproba(indwrong(j)),'%1.3f'),' p-value = ', num2str(proba(indwrong(j)),'%1.3f')])
|
||||
end
|
||||
skipline()
|
||||
if ~isempty(indwrong)
|
||||
stab_map_1(lpmat, [1:Nsam], iwrong, awronguniname, 1, indwrong, OutputDirectoryName,[],awrongunititle);
|
||||
end
|
||||
end
|
||||
|
||||
skipline()
|
||||
if ~isempty(indunst)
|
||||
stab_map_1(lpmat, [1:Nsam], ixun, aunstablename, 1, indunst, OutputDirectoryName,[],aunstabletitle);
|
||||
disp('Starting bivariate analysis:')
|
||||
|
||||
c0=corrcoef(lpmat(istable,:));
|
||||
c00=tril(c0,-1);
|
||||
|
||||
if length(istable)>10,
|
||||
stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName,xparam1,astitle);
|
||||
end
|
||||
end
|
||||
|
||||
if ~isempty(iwrong),
|
||||
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iwrong, awronguniname,0);
|
||||
% indwrong=find(dproba>ksstat);
|
||||
indwrong=find(proba<pvalue_ks);
|
||||
disp('Smirnov statistics in driving no solution')
|
||||
for j=1:length(indwrong),
|
||||
disp([M_.param_names(estim_params_.param_vals(indwrong(j),1),:),' d-stat = ', num2str(dproba(indwrong(j)),'%1.3f'),' p-value = ', num2str(proba(indwrong(j)),'%1.3f')])
|
||||
if length(iunstable)>10,
|
||||
stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName,xparam1,autitle);
|
||||
end
|
||||
skipline()
|
||||
if ~isempty(indwrong)
|
||||
stab_map_1(lpmat, [1:Nsam], iwrong, awronguniname, 1, indwrong, OutputDirectoryName,[],awrongunititle);
|
||||
if length(iindeterm)>10,
|
||||
stab_map_2(lpmat(iindeterm,:),alpha2, pvalue_corr, aindname, OutputDirectoryName,xparam1,aindtitle);
|
||||
end
|
||||
end
|
||||
|
||||
skipline()
|
||||
disp('Starting bivariate analysis:')
|
||||
|
||||
c0=corrcoef(lpmat(istable,:));
|
||||
c00=tril(c0,-1);
|
||||
|
||||
if length(istable)>10,
|
||||
stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName,xparam1,astitle);
|
||||
end
|
||||
if length(iunstable)>10,
|
||||
stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName,xparam1,autitle);
|
||||
end
|
||||
if length(iindeterm)>10,
|
||||
stab_map_2(lpmat(iindeterm,:),alpha2, pvalue_corr, aindname, OutputDirectoryName,xparam1,aindtitle);
|
||||
end
|
||||
if length(ixun)>10,
|
||||
stab_map_2(lpmat(ixun,:),alpha2, pvalue_corr, aunstname, OutputDirectoryName,xparam1,aunsttitle);
|
||||
end
|
||||
if length(iwrong)>10,
|
||||
stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName,xparam1,awrongtitle);
|
||||
end
|
||||
|
||||
x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
|
||||
x0 = [x0; lpmat(istable(1),:)'];
|
||||
if istable(end)~=Nsam
|
||||
M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)';
|
||||
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
||||
% stoch_simul([]);
|
||||
end
|
||||
else
|
||||
if length(iunstable)==0,
|
||||
disp('All parameter values in the specified ranges give unique saddle-path solution!')
|
||||
if length(ixun)>10,
|
||||
stab_map_2(lpmat(ixun,:),alpha2, pvalue_corr, aunstname, OutputDirectoryName,xparam1,aunsttitle);
|
||||
end
|
||||
if length(iwrong)>10,
|
||||
stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName,xparam1,awrongtitle);
|
||||
end
|
||||
|
||||
x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
|
||||
x0 = [x0; lpmat(istable(1),:)'];
|
||||
if istable(end)~=Nsam
|
||||
M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)';
|
||||
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
||||
% stoch_simul([]);
|
||||
end
|
||||
else
|
||||
disp('All parameter values in the specified ranges are not acceptable!')
|
||||
x0=[];
|
||||
end
|
||||
else
|
||||
disp('All parameter values in the specified ranges give unique saddle-path solution!')
|
||||
x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
|
||||
x0 = [x0; lpmat(istable(1),:)'];
|
||||
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,97 @@
|
|||
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
|
||||
varexo e_R e_q e_ys e_pies e_A;
|
||||
|
||||
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
|
||||
|
||||
psi1 = 1.54;
|
||||
psi2 = 0.25;
|
||||
psi3 = 0.25;
|
||||
rho_R = 0.5;
|
||||
alpha = 0.3;
|
||||
rr = 2.51;
|
||||
k = 0.5;
|
||||
tau = 0.5;
|
||||
rho_q = 0.4;
|
||||
rho_A = 0.2;
|
||||
rho_ys = 0.9;
|
||||
rho_pies = 0.7;
|
||||
|
||||
|
||||
model(linear);
|
||||
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
|
||||
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
|
||||
pie = de+(1-alpha)*dq+pie_s;
|
||||
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
|
||||
dq = rho_q*dq(-1)+e_q;
|
||||
y_s = rho_ys*y_s(-1)+e_ys;
|
||||
pie_s = rho_pies*pie_s(-1)+e_pies;
|
||||
A = rho_A*A(-1)+e_A;
|
||||
y_obs = y-y(-1)+A;
|
||||
pie_obs = 4*pie;
|
||||
R_obs = 4*R;
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var e_R = 1.25^2;
|
||||
var e_q = 2.5^2;
|
||||
var e_A = 1.89;
|
||||
var e_ys = 1.89;
|
||||
var e_pies = 1.89;
|
||||
end;
|
||||
|
||||
varobs y_obs R_obs pie_obs dq de;
|
||||
|
||||
estimated_params;
|
||||
psi1 , gamma_pdf,1.5,0.5;
|
||||
psi2 , gamma_pdf,0.25,0.125;
|
||||
psi3 , gamma_pdf,0.25,0.125;
|
||||
rho_R ,beta_pdf,0.5,0.2;
|
||||
alpha ,beta_pdf,0.3,0.1;
|
||||
rr ,gamma_pdf,2.5,1;
|
||||
k , gamma_pdf,0.5,0.25;
|
||||
tau ,gamma_pdf,0.5,0.2;
|
||||
rho_q ,beta_pdf,0.4,0.2;
|
||||
rho_A ,beta_pdf,0.5,0.2;
|
||||
rho_ys ,beta_pdf,0.8,0.1;
|
||||
rho_pies,beta_pdf,0.7,0.15;
|
||||
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
|
||||
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
|
||||
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
|
||||
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
|
||||
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
|
||||
end;
|
||||
|
||||
// endogenous prior restrictions
|
||||
options_.relative_irf=1;
|
||||
irf_calibration;
|
||||
y(1:4), e_ys, [ -50 50]; //[first year response]
|
||||
//y(1:4), e_ys, [-inf -50]; //[first year response]
|
||||
@#for ilag in 21:40
|
||||
R_obs(@{ilag}), e_ys, [0 6]; //[response after 4th year to 10th year]
|
||||
@#endfor
|
||||
end;
|
||||
|
||||
/*
|
||||
irf_calibration;
|
||||
y(1:4), e_ys, [-inf -0.4]; //[first year response]
|
||||
@#for ilag in 21:40
|
||||
R_obs(@{ilag}), e_ys, [0 0.25]; //[response after 4th year to 10th year]
|
||||
@#endfor
|
||||
end;
|
||||
*/
|
||||
|
||||
moment_calibration;
|
||||
//y_obs,y_obs, [0.8 1.1]; //[unconditional variance]
|
||||
y_obs,y_obs(-(1:4)), +; //[first year acf]
|
||||
@#for ilag in 0:1
|
||||
y_obs,R_obs(-@{ilag}), -; //[ccf]
|
||||
@#endfor
|
||||
end;
|
||||
|
||||
dynare_sensitivity(prior_range=0);
|
||||
/*
|
||||
estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2, mode_file = ls2003a_mode,
|
||||
prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=0, mh_drop=0.6, bayesian_irf);
|
||||
//stoch_simul(irf=40, order=1, relative_irf) y_obs R_obs pie_obs dq de;
|
||||
stoch_simul(irf=40, order=1) y_obs R_obs pie_obs dq de;
|
||||
*/
|
||||
Loading…
Reference in New Issue