TeX formatting and various things...
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@656 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
adjemian
parent
00fac4544c
commit
58cb6e42b6
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@ -191,8 +191,7 @@ rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet
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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,:), ...
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'exact')];
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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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@ -257,29 +256,9 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
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disp(sprintf('Objective function at mode: %f',fval))
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disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data)))
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elseif options_.mode_compute == 5
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if isfield(options_,'hess')
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flag = options_.hess;
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else
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flag = 1;
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end
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if ~exist('igg'), % by M. Ratto
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hh=[];
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gg=[];
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igg=[];
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end % by M. Ratto
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if isfield(options_,'ftol')
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crit = options_.ftol;
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else
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crit = 1.e-7;
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end
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if isfield(options_,'nit')
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nit = options_.nit;
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else
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nit=1000;
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end
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%[xparam1, hh, gg, fval] = newrat('DsgeLikelihood',xparam1,[],[],flag,gend,data);
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[xparam1, hh, gg, fval, invhess] = newrat('DsgeLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend,data);
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eval(['save ' M_.fname '_mode xparam1 hh gg fval invhess;']);
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flag = 0;
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[xparam1, hh, gg, fval] = newrat('DsgeLikelihood',xparam1,[],[],flag,gend,data);
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eval(['save ' M_.fname '_mode xparam1 hh gg fval;']);
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end
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if options_.mode_compute ~= 5
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hh = reshape(hessian('DsgeLikelihood',xparam1,gend,data),nx,nx);
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@ -483,9 +462,11 @@ elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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end
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OutputDirectoryName = CheckPath('Output');
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if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior mode) Latex output
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if np
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filename = [M_.fname '_Posterior_Mode_1.TeX'];
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filename = [OutputDirectoryName '\' M_.fname '_Posterior_Mode_1.TeX'];
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fidTeX = fopen(filename,'w');
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fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (parameters)\n');
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@ -502,7 +483,7 @@ if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior m
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ip = nvx+nvn+ncx+ncn+1;
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for i=1:np
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fprintf(fidTeX,'$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
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M_.param_names_tex(estim_params_.param_vals(i,1),:),...%deblank(estim_params_.tex(i,:)),...
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M_.param_names_tex(estim_params_.param_vals(i,1),:),...
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deblank(pnames(bayestopt_.pshape(ip)+1,:)),...
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bayestopt_.pmean(ip),...
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estim_params_.param_vals(i,6),...
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@ -520,7 +501,7 @@ if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior m
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fclose(fidTeX);
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end
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if nvx
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TeXfile = [M_.fname '_Posterior_Mode_2.TeX'];
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TeXfile = [OutputDirectoryName '\' M_.fname '_Posterior_Mode_2.TeX'];
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fidTeX = fopen(TeXfile,'w');
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fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (standard deviation of structural shocks)\n');
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@ -556,7 +537,7 @@ if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior m
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fclose(fidTeX);
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end
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if nvn
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TeXfile = [M_.fname '_Posterior_Mode_3.TeX'];
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TeXfile = [OutputDirectoryName '\' M_.fname '_Posterior_Mode_3.TeX'];
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fidTeX = fopen(TeXfile,'w');
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fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (standard deviation of measurement errors)\n');
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@ -590,7 +571,7 @@ if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior m
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fclose(fidTeX);
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end
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if ncx
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TeXfile = [M_.fname '_Posterior_Mode_4.TeX'];
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TeXfile = [OutputDirectoryName '\' M_.fname '_Posterior_Mode_4.TeX'];
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fidTeX = fopen(TeXfile,'w');
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fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (correlation of structural shocks)\n');
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@ -625,7 +606,7 @@ if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior m
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fclose(fidTeX);
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end
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if ncn
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TeXfile = [M_.fname '_Posterior_Mode_5.TeX'];
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TeXfile = [OutputDirectoryName '\' M_.fname '_Posterior_Mode_5.TeX'];
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fidTeX = fopen(TeXfile,'w');
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fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (correlation of measurement errors)\n');
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@ -4,7 +4,7 @@ global M_ oo_ options_ bayestopt_ estim_params_
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bayestopt_.penalty = 1e8;
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DirectoryName = CheckPath('metropolis');
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MhDirectoryName = CheckPath('metropolis');
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nblck = options_.mh_nblck;
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nruns = ones(nblck,1)*options_.mh_replic;
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@ -23,9 +23,9 @@ if options_.load_mh_file == 0
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disp('MH: One Chain mode.')
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end
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% Delete old mh files...
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files = eval(['dir(''' DirectoryName '/' M_.fname '_mh*_blck*.mat'');']);
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files = dir([ MhDirectoryName '/' M_.fname '_mh*_blck*.mat']);
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if length(files)
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delete([ DirectoryName '/' M_.fname '_mh*_blck*.mat']);
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delete([ MhDirectoryName '/' M_.fname '_mh*_blck*.mat']);
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disp('MH: Old _mh files succesfully erased!')
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end
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% Initial values...
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@ -78,9 +78,9 @@ if options_.load_mh_file == 0
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fline = ones(nblck,1);
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NewFile = ones(nblck,1);
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% Creation of the mh-history file:
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file = eval(['dir(''' DirectoryName '/' M_.fname '_mh_history.mat'');']);
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file = dir([MhDirectoryName '/' M_.fname '_mh_history.mat']);
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if length(files)
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delete([ DirectoryName '/' M_.fname '_mh_history.mat']);
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delete([ MhDirectoryName '/' M_.fname '_mh_history.mat']);
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disp('MH: Old mh_history file succesfully erased!')
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end
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AnticipatedNumberOfFiles = floor(nruns(1)/MAX_nruns);
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@ -102,8 +102,8 @@ if options_.load_mh_file == 0
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save([DirectoryName '/' M_.fname '_mh_history'],'record');
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elseif options_.load_mh_file == 1% Here we consider previous mh files (previous mh did not crash).
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disp('MH: I''m loading past metropolis-hastings simulations...')
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file = eval(['dir(''' DirectoryName '/' M_.fname '_mh_history.mat'');']);
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files = eval(['dir(''' DirectoryName '/' M_.fname '_mh*.mat'');']);
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file = dir([ MhDirectoryName '/' M_.fname '_mh_history.mat' ]);
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files = dir([ MhDirectoryName '/' M_.fname '_mh*.mat']);
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if ~length(files)
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disp('MH:: FAILURE! there is no MH file to load here!')
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return
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@ -112,7 +112,7 @@ elseif options_.load_mh_file == 1% Here we consider previous mh files (previous
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disp('MH:: FAILURE! there is no MH-history file!')
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return
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else
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load([ DirectoryName '/' M_.fname '_mh_history'])
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load([ MhDirectoryName '/' M_.fname '_mh_history'])
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end
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past_number_of_blocks = record.Nblck;
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if past_number_of_blocks ~= nblck
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@ -148,7 +148,7 @@ elseif options_.load_mh_file == 1% Here we consider previous mh files (previous
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record.MhDraws(end,3) = AnticipatedNumberOfLinesInTheLastFile;
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randn('state',record.Seeds.Normal);
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rand('state',record.Seeds.Unifor);
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save([DirectoryName '/' M_.fname '_mh_history'],'record');
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save([MhDirectoryName '/' M_.fname '_mh_history'],'record');
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disp(['MH: ... It''s done. I''ve loaded ' int2str(NumberOfPreviousSimulations) ' simulations.'])
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disp(' ')
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elseif options_.load_mh_file == -1% The previous metropolis-hastings
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@ -156,13 +156,13 @@ elseif options_.load_mh_file == -1% The previous metropolis-hastings
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% recover the saved draws...
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disp('MH: Recover mode!')
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disp(' ')
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file = eval(['dir(''' DirectoryName '/' M_.fname '_mh_history.mat'');']);
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file = dir([MhDirectoryName '/' M_.fname '_mh_history.mat']);
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if ~length(file)
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disp('MH:: FAILURE! there is no MH-history file!')
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return
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else
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load([ DirectoryName '/' M_.fname '_mh_history'])
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end
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load([ MhDirectoryName '/' M_.fname '_mh_history'])
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end
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nblck = record.Nblck;
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options_.mh_nblck = nblck;
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if size(record.MhDraws,1) == 1
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@ -202,12 +202,12 @@ elseif options_.load_mh_file == -1% The previous metropolis-hastings
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ExpectedNumberOfMhFilesPerBlock = sum(record.MhDraws(:,2),1);
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ExpectedNumberOfMhFiles = ExpectedNumberOfMhFilesPerBlock*nblck;
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% I count the total number of saved mh files...
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AllMhFiles = eval(['dir(''' DirectoryName '/' M_.fname '_mh*_blck*.mat'');']);
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AllMhFiles = dir([MhDirectoryName '/' M_.fname '_mh*_blck*.mat']);
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TotalNumberOfMhFiles = length(AllMhFiles);
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% I count the number of saved mh files per block
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NumberOfMhFilesPerBlock = zeros(nblck,1);
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for i = 1:nblck
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BlckMhFiles = eval(['dir(''' DirectoryName '/' M_.fname '_mh*_blck' int2str(i) '.mat'');']);
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BlckMhFiles = dir([ MhDirectoryName '/' M_.fname '_mh*_blck' int2str(i) '.mat']);
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NumberOfMhFilesPerBlock(i) = length(BlckMhFiles);
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end
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tmp = NumberOfMhFilesPerBlock(1); b = 1;
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@ -229,8 +229,7 @@ elseif options_.load_mh_file == -1% The previous metropolis-hastings
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% (if there was a complete session before the crash) ?
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if OldMh
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ante = sum(record.MhDraws(1:end-1,2),1);
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load(['./' DirectoryName '/' M_.fname '_mh' int2str(ante) '_blck' ...
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int2str(CrashedBlck) '.mat'],'logpo2');
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load([MhDirectoryName '/' M_.fname '_mh' int2str(ante) '_blck' int2str(CrashedBlck) '.mat'],'logpo2');
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if length(logpo2) == MAX_nruns
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IsTheLastFileOfThePreviousMhFull = 1;
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else
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@ -245,7 +244,7 @@ elseif options_.load_mh_file == -1% The previous metropolis-hastings
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MhFileNumber = ante;
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while MhFileExist
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MhFileNumber = MhFileNumber + 1;
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if ~exist(['./' DirectoryName '/' M_.fname '_mh' int2str(MhFileNumber) '_blck' int2str(CrashedBlck) '.mat'])
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if ~exist([MhDirectoryName '/' M_.fname '_mh' int2str(MhFileNumber) '_blck' int2str(CrashedBlck) '.mat'])
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MhFileExist = 0;
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end
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end
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@ -267,7 +266,7 @@ elseif options_.load_mh_file == -1% The previous metropolis-hastings
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nruns(CrashedBlck) = nruns(CrashedBlck)-NumberOfSavedDraws;
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% I initialize with the last saved mh file of the inccomplete
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% block:
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load(['./' DirectoryName '/' M_.fname '_mh' int2str(MhFileNumber-1) '_blck' int2str(CrashedBlck) '.mat']);
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load([MhDirectoryName '/' M_.fname '_mh' int2str(MhFileNumber-1) '_blck' int2str(CrashedBlck) '.mat']);
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ilogpo2(CrashedBlck) = logpo2(end);
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ix2(CrashedBlck,:) = x2(end,:);
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NewFile(CrashedBlck) = MhFileNumber;
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@ -319,7 +318,7 @@ for b = fblck:nblck
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prtfrc = j/nruns(b);
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waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
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if (irun == InitSizeArray(b)) | (j == nruns(b)) % Now I save the simulations
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save([DirectoryName '/' M_.fname '_mh' int2str(NewFile(b)) '_blck' int2str(b)],'x2','logpo2');
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save([MhDirectoryName '/' M_.fname '_mh' int2str(NewFile(b)) '_blck' int2str(b)],'x2','logpo2');
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InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
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if j == nruns(b) % I record the last draw...
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record.LastParameters(b,:) = x2(end,:);
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@ -342,8 +341,8 @@ for b = fblck:nblck
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end% End of the loop over the mh-blocks.
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record.Seeds.Normal = randn('state');
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record.Seeds.Unifor = rand('state');
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save([DirectoryName '/' M_.fname '_mh_history'],'record');
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save([MhDirectoryName '/' M_.fname '_mh_history'],'record');
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disp(['MH: Number of mh files : ' int2str(NewFile(1)) ' per block.'])
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disp(['MH: Total number of generated files : ' int2str(NewFile(1)*nblck) '.'])
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disp(['MH: Total number of iterations : ' int2str((NewFile(1)-1)*MAX_nruns+irun-1) '.'])
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disp(' ')
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disp(' ')
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@ -39,6 +39,5 @@ else
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bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
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kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
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optimal_bandwidth = mh_optimal_bandwidth(xx,length(xx),bandwidth,kernel_function);
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% [abscissa,f] = kernel_density_estimate(,,,)
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[density(:,1),density(:,2)] = kernel_density_estimate(xx,number_of_grid_points,optimal_bandwidth,kernel_function);
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end
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