method_of_moments: add irf_matching to objective_function
Willi Mutschler
parent
e9871d7d47
commit
20ec0a6c97
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@ -1,7 +1,8 @@
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function [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moments_params_derivs] = objective_function(xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moments_params_derivs] = objective_function(xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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function [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moments_params_derivs, irf_model_varobs] = objective_function(xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moments_params_derivs, irf_model_varobs] = objective_function(xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% -------------------------------------------------------------------------
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% This function evaluates the objective function for a method of moments estimation
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% This function evaluates the objective function for method of moments estimation,
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% i.e. distance between model and data moments/irfs, possibly augmented with priors.
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% -------------------------------------------------------------------------
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% INPUTS (same ones as in dsge_likelihood.m and dsge_var_likelihood.m)
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% - xparam: [vector] current value of estimated parameters as returned by set_prior()
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@ -26,6 +27,7 @@ function [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moment
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% - Q: [double] value of the quadratic form of the moment difference
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% - model_moments: [vector] model moments
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% - model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
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% - irf_model_varobs: [matrix] model irfs for observable variables (used for plotting matched irfs in mom.run)
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% -------------------------------------------------------------------------
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% This function is called by
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% o mom.run
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@ -33,8 +35,12 @@ function [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moment
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% -------------------------------------------------------------------------
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% This function calls
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% o check_bounds_and_definiteness_estimation
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% o get_lower_cholesky_covariance
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% o get_perturbation_params_derivs
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% o getIrfShocksIndx
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% o irf
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% o mom.get_data_moments
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% o priordens
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% o pruned_state_space_system
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% o resol
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% o set_all_parameters
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@ -62,6 +68,7 @@ function [fval, info, exit_flag, df, junkHessian, Q, model_moments, model_moment
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%------------------------------------------------------------------------------
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% Initialization of the returned variables and others...
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%------------------------------------------------------------------------------
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irf_model_varobs = [];
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model_moments_params_derivs = [];
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model_moments = [];
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Q = [];
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@ -247,12 +254,84 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
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end
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%------------------------------------------------------------------------------
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% IRF_MATCHING using STOCH_SIMUL: Compute irfs given model solution and Cholesky
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% decomposition on shock covariance matrix; this resembles the core codes in
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% stoch_simul
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%------------------------------------------------------------------------------
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if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING') && strcmp(options_mom_.mom.simulation_method,'STOCH_SIMUL')
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cs = get_lower_cholesky_covariance(M_.Sigma_e,options_mom_.add_tiny_number_to_cholesky);
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irf_shocks_indx = getIrfShocksIndx(M_, options_mom_);
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modelIrf = nan(options_mom_.irf,M_.endo_nbr,M_.exo_nbr);
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for i = irf_shocks_indx
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if options_mom_.order>1 && options_mom_.relative_irf % normalize shock to 0.01 before IRF generation for GIRFs; multiply with 100 later
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y_irf = irf(M_, options_mom_, dr, cs(:,i)./cs(i,i)/100, options_mom_.irf, options_mom_.drop, options_mom_.replic, options_mom_.order);
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else % for linear model, rescaling is done later
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y_irf = irf(M_, options_mom_, dr, cs(:,i), options_mom_.irf, options_mom_.drop, options_mom_.replic, options_mom_.order);
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end
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if any(any(isnan(y_irf))) && ~options_mom_.pruning && ~(options_mom_.order==1)
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fprintf('\nirf_matching: The simulations conducted for generating IRFs to %s were explosive: Either reduce the shock size, use pruning, or set the approximation order to 1.\n', M_.exo_names{i})
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fval = Inf;
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info(1) = 180;
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info(4) = 0.1;
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exit_flag = 0;
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if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
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fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
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end
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return
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end
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if options_mom_.relative_irf
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if options_mom_.order==1 % multiply with 100 for backward compatibility
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y_irf = 100*y_irf/cs(i,i);
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end
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end
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modelIrf(:,:,i) = transpose(y_irf);
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end
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% do transformations on model irfs if irf_matching_file is provided
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if ~isempty(options_mom_.mom.irf_matching_file.name)
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[modelIrf,check] = feval(str2func(options_mom_.mom.irf_matching_file.name),modelIrf, M_, options_mom_);
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if check
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fval = Inf;
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info(1) = 180;
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info(4) = 0.1;
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exit_flag = 0;
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if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
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fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
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end
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return
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end
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end
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irf_model_varobs = modelIrf(:,options_mom_.varobs_id,:); % focus only on observables (this will be used later for plotting)
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model_moments = irf_model_varobs(options_mom_.mom.irfIndex); % focus only on selected irf periods
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end
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%--------------------------------------------------------------------------
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% Compute quadratic target function
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%--------------------------------------------------------------------------
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moments_difference = data_moments - model_moments;
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if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
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if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
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Q = transpose(moments_difference)*weighting_info.W*moments_difference;
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% log-likelihood
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lnlik = options_mom_.mom.mom_nbr/2*log(1/2/pi) - 1/2*weighting_info.Winv_logdet - 1/2*Q;
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if isinf(lnlik)
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fval = Inf; info(1) = 50; info(4) = 0.1; exit_flag = 0;
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return
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end
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if isnan(lnlik)
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fval = Inf; info(1) = 45; info(4) = 0.1; exit_flag = 0;
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return
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end
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if imag(lnlik)~=0
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fval = Inf; info(1) = 46; info(4) = 0.1; exit_flag = 0;
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return
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end
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% add log prior if necessary
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lnprior = priordens(xparam,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
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fval = - (lnlik + lnprior);
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elseif strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
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residuals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*weighting_info.Sw*moments_difference;
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Q = residuals'*residuals;
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if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
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