Merge pull request #1015 from JohannesPfeifer/model_comparison
Bugfixes and improvements for model_comparisontime-shift
Stéphane Adjemian
commit
3490e400bc
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@ -5840,12 +5840,14 @@ Standard deviation of structural shocks
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@defvr {MATLAB/Octave variable} oo_.MarginalDensity.LaplaceApproximation
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Variable set by the @code{estimation} command.
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Variable set by the @code{estimation} command. Stores the marginal data density
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based on the Laplace Approximation.
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@end defvr
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@defvr {MATLAB/Octave variable} oo_.MarginalDensity.ModifiedHarmonicMean
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Variable set by the @code{estimation} command, if it is used with
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@code{mh_replic > 0} or @code{load_mh_file} option.
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@code{mh_replic > 0} or @code{load_mh_file} option. Stores the marginal data density
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based on @cite{Geweke (1999)} Modified Harmonic Mean estimator.
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@end defvr
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@defvr {MATLAB/Octave variable} oo_.FilteredVariables
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@ -6180,6 +6182,20 @@ Dynare: it is the user's responsibility to make sure that model comparison is
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based on proper priors. Note that, for obvious reasons, this is not an issue if
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the compared marginal densities are based on Laplace approximations.
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@optionshead
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@table @code
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@item marginal_density = @var{ESTIMATOR}
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Specifies the estimator for computing the marginal data density. @var{ESTIMATOR} can
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take one of the following two values:
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@code{laplace} for the Laplace estimator or @code{modifiedharmonicmean} for the
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@cite{Geweke (1999)} Modified Harmonic Mean estimator. Default value: @code{laplace}
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@end table
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@outputhead
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The results are stored in @code{oo_.Model_Comparison}, which is described below.
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@examplehead
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@ -6191,6 +6207,32 @@ over @code{alt_model}.
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@end deffn
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@defvr {MATLAB/Octave variable} oo_.Model_Comparison
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Variable set by the @code{model_comparison} command. Fields are of the form:
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@example
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@code{oo_.Model_Comparison.@var{FILENAME}.@var{VARIABLE_NAME}}
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@end example
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where @var{FILENAME} is the file name of the model and @var{VARIABLE_NAME} is one of the following:
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@table @code
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@item Prior
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(Normalized) prior density over the model
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@item Log_Marginal_Density
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Logarithm of the marginal data density
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@item Bayes_Ratio
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Ratio of the marginal data density of the model relative to the one of the first declared model
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@item Posterior_Model_Probability
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Posterior probability of the respective model
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@end table
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@end defvr
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@deffn Command shock_decomposition [@var{VARIABLE_NAME}]@dots{};
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@deffnx Command shock_decomposition (@var{OPTIONS}@dots{}) [@var{VARIABLE_NAME}]@dots{};
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@ -1,18 +1,26 @@
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function PosteriorOddsTable = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
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% Bayesian model comparison. This function computes Odds ratios and
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% estimate a posterior density over a colletion of models.
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function oo = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
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% function oo = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
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% Conducts Bayesian model comparison. This function computes Odds ratios and
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% estimates a posterior density over a collection of models.
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%
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% INPUTS
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% ModelNames [string] m*1 cell array of string.
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% ModelPriors [double] m*1 vector of prior probabilities
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% oo [struct] Dynare results structure
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% options_ [struct] Dynare options structure
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% fname [string] name of the current mod-file
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%
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% OUTPUTS
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% none
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% oo [struct] Dynare results structure containing the
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% results in a field PosteriorOddsTable
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%
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% ALGORITHM
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% See e.g. Koop (2003): Bayesian Econometrics
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2007-2011 Dynare Team
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% Copyright (C) 2007-2015 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -39,21 +47,28 @@ else % The prior density has to sum up to one.
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prior_flag = 1;
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improper = abs(sum(ModelPriors)-1)>1e-6;
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if improper
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disp('model_comparison:: The user supplied prior distribution over models is improper...')
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disp('model_comparison:: The distribution is automatically rescaled!')
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if ~all(ModelPriors==1)
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disp('model_comparison:: The user supplied prior distribution over models is improper...')
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disp('model_comparison:: The distribution is automatically rescaled!')
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end
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ModelPriors=ModelPriors/sum(ModelPriors);
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end
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end
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% The marginal densities are based on Laplace approxiations (default) or
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% modified harmonic mean estimators.
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if isfield(options_,'model_comparison_approximation')
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type = options_.model_comparison_approximation;
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if strcmp(type,'Laplace')
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if isfield(options_,'mc_marginal_density')
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type = options_.mc_marginal_density;
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if strcmp(type,'laplace') || strcmp(type,'Laplace')
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type = 'LaplaceApproximation';
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title = 'Model Comparison (based on Laplace approximation)';
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elseif strcmp(type,'modifiedharmonicmean') || strcmp(type,'ModifiedHarmonicMean')
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type = 'ModifiedHarmonicMean';
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title = 'Model Comparison (based on Modified Harmonic Mean Estimator)';
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end
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else
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type = 'LaplaceApproximation';
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title = 'Model Comparison (based on Laplace approximation)';
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end
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% Get the estimated logged marginal densities.
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@ -98,22 +113,29 @@ lmpd = log(ModelPriors)+MarginalLogDensity;
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[maxval,k] = max(lmpd);
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elmpd = exp(lmpd-maxval);
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% Now I display the posterior probabilities.
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title = 'Model Comparison';
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headers = char('Model',ShortModelNames{:});
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if prior_flag
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labels = char('Priors','Log Marginal Density','Bayes Ratio', ...
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'Posterior Model Probability');
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field_labels={'Prior','Log_Marginal_Density','Bayes_Ratio', ...
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'Posterior_Model_Probability'};
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values = [ModelPriors';MarginalLogDensity';exp(lmpd-lmpd(1))'; ...
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elmpd'/sum(elmpd)];
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else
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labels = char('Priors','Log Marginal Density','Bayes Ratio','Posterior Odds Ratio', ...
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'Posterior Model Probability');
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field_labels={'Prior','Log_Marginal_Density','Bayes_Ratio','Posterior_Odds_Ratio','Posterior_Model_Probability'};
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values = [ModelPriors';MarginalLogDensity'; exp(MarginalLogDensity-MarginalLogDensity(1))'; ...
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exp(lmpd-lmpd(1))'; elmpd'/sum(elmpd)];
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end
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for model_iter=1:NumberOfModels
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for var_iter=1:size(labels,1)
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oo.Model_Comparison.(deblank(headers(1+model_iter,:))).(field_labels{var_iter})=values(var_iter,model_iter);
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end
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end
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dyntable(title,headers,labels,values, 0, 15, 6);
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@ -1171,7 +1171,7 @@ ModelComparisonStatement::writeOutput(ostream &output, const string &basename, b
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output << "ModelNames_ = { ModelNames_{:} '" << (*it).first << "'};" << endl;
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output << "ModelPriors_ = [ ModelPriors_ ; " << (*it).second << "];" << endl;
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}
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output << "model_comparison(ModelNames_,ModelPriors_,oo_,options_,M_.fname);" << endl;
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output << "oo_ = model_comparison(ModelNames_,ModelPriors_,oo_,options_,M_.fname);" << endl;
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}
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PlannerObjectiveStatement::PlannerObjectiveStatement(StaticModel *model_tree_arg) :
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@ -3,6 +3,7 @@ MODFILES = \
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estimation/fs2000_MCMC_jumping_covariance.mod \
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estimation/fs2000_initialize_from_calib.mod \
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estimation/fs2000_calibrated_covariance.mod \
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estimation/fs2000_model_comparison.mod \
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estimation/MH_recover/fs2000_recover.mod \
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estimation/t_proposal/fs2000_student.mod \
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estimation/TaRB/fs2000_tarb.mod \
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@ -298,6 +299,9 @@ AIM/fs2000x10_L9_L_AIM.o.trs: AIM/fs2000x10_L9_L.o.trs
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AIM/ls2003_2L0L_AIM.o.trs: AIM/ls2003_2L0L.o.trs
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AIM/ls2003_2L2L_AIM.o.trs: AIM/ls2003_2L2L.o.trs
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estimation/fs2000_model_comparison.m.trs: estimation/fs2000.m.trs estimation/fs2000_initialize_from_calib.m.trs estimation/fs2000_calibrated_covariance.m.trs
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estimation/fs2000_model_comparison.o.trs: estimation/fs2000.o.trs estimation/fs2000_initialize_from_calib.o.trs estimation/fs2000_calibrated_covariance.o.trs
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optimizers/fs2000_102.m.trs: estimation/fs2000.m.trs
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optimizers/fs2000_102.o.trs: estimation/fs2000.o.trs
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@ -0,0 +1,110 @@
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// See fs2000.mod in the examples/ directory for details on the model
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var m P c e W R k d n l gy_obs gp_obs y dA;
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varexo e_a e_m;
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parameters alp bet gam mst rho psi del;
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alp = 0.33;
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bet = 0.99;
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gam = 0.003;
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mst = 1.011;
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rho = 0.7;
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psi = 0.787;
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del = 0.02;
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model;
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dA = exp(gam+e_a);
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
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W = l/n;
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
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R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
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P*c = m;
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m-1+d = l;
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e = exp(e_a);
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y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
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gy_obs = dA*y/y(-1);
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gp_obs = (P/P(-1))*m(-1)/dA;
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end;
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steady_state_model;
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dA = exp(gam);
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gst = 1/dA;
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m = mst;
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khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
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xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
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nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
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n = xist/(nust+xist);
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P = xist + nust;
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k = khst*n;
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l = psi*mst*n/( (1-psi)*(1-n) );
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c = mst/P;
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d = l - mst + 1;
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y = k^alp*n^(1-alp)*gst^alp;
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R = mst/bet;
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W = l/n;
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ist = y-c;
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q = 1 - d;
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e = 1;
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gp_obs = m/dA;
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gy_obs = dA;
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end;
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shocks;
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var e_a; stderr 0.014;
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var e_m; stderr 0.005;
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end;
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steady;
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check;
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estimated_params;
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% alp, beta_pdf, 0.356, 0.02;
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bet, beta_pdf, 0.993, 0.002;
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% gam, normal_pdf, 0.0085, 0.003;
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% mst, normal_pdf, 1.0002, 0.007;
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% rho, beta_pdf, 0.129, 0.223;
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% psi, beta_pdf, 0.65, 0.05;
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% del, beta_pdf, 0.01, 0.005;
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% stderr e_a, inv_gamma_pdf, 0.035449, inf;
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% stderr e_m, inv_gamma_pdf, 0.008862, inf;
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end;
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varobs gp_obs gy_obs;
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options_.solve_tolf = 1e-12;
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estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=1,mh_jscale=0.8);
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model_comparison fs2000(0.5) fs2000_calibrated_covariance(0.5);
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if isfield(oo_,'Model_Comparison')
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error('Model Comparison should not work with ML')
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end
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model_comparison fs2000 fs2000_initialize_from_calib;
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model_comparison fs2000(0.5) fs2000_initialize_from_calib(0.5);
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model_comparison fs2000(0.25) fs2000_initialize_from_calib(0.25);
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model_comparison (marginal_density=laplace) fs2000(0.5) fs2000_initialize_from_calib(0.5);
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if oo_.Model_Comparison.fs2000.Posterior_Model_Probability < oo_.Model_Comparison.fs2000_initialize_from_calib.Posterior_Model_Probability && ...
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oo_.Model_Comparison.fs2000.Log_Marginal_Density > oo_.Model_Comparison.fs2000_initialize_from_calib.Log_Marginal_Density
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error('Model Comparison not correct')
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end
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oo_laplace=oo_;
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model_comparison (marginal_density=modifiedharmonicmean) fs2000(0.5) fs2000_initialize_from_calib(0.75);
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if abs(oo_laplace.Model_Comparison.fs2000.Log_Marginal_Density-oo_.Model_Comparison.fs2000.Log_Marginal_Density)>0.1
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error('Laplace and Harmonic Mean do not match')
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end
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model_comparison (marginal_density=modifiedharmonicmean) fs2000(0) fs2000_initialize_from_calib(1);
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if oo_.Model_Comparison.fs2000.Posterior_Model_Probability~=0 && oo_.Model_Comparison.fs2000_initialize_from_calib.Log_Marginal_Density~=1
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error('Incorporation of prior wrong')
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end
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model_comparison(marginal_density=laplace) fs2000(0.333) fs2000_initialize_from_calib(0.333) fs2000_model_comparison(0.333);
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