Remove matched_moments_ as input argument as it is now a field of M_
Johannes Pfeifer
parent
6cf8f27d94
commit
96be513ecf
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@ -1,5 +1,5 @@
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function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, matched_moments_, options_mom_)
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%function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, matched_moments_, options_mom_)
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function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
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%function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
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% -------------------------------------------------------------------------
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% This function performs a method of moments estimation with the following steps:
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% Step 0: Check if required structures and options exist
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@ -26,11 +26,11 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
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% o options_: [structure] information about global options
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% o oo_: [structure] storage for results
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% o estim_params_: [structure] information about estimated parameters
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% o M_: [structure] information about model
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% o matched_moments_: [cell] information about selected moments to match in estimation
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% vars: matched_moments_{:,1});
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% lead/lags: matched_moments_{:,2};
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% powers: matched_moments_{:,3};
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% o M_: [structure] information about model with
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% o matched_moments: [cell] information about selected moments to match in estimation
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% vars: matched_moments{:,1});
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% lead/lags: matched_moments{:,2};
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% powers: matched_moments{:,3};
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% o options_mom_: [structure] information about settings specified by the user
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% -------------------------------------------------------------------------
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% OUTPUTS
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@ -111,7 +111,7 @@ if isempty(estim_params_) % structure storing the info about estimated parameter
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error('method_of_moments: The ''estimated_params'' block must not be empty')
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end
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end
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if isempty(matched_moments_) % structure storing the moments used for the method of moments estimation
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if isempty(M_.matched_moments) % structure storing the moments used for the method of moments estimation
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error('method_of_moments: You need to provide a ''matched_moments'' block')
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end
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if ~isempty(bayestopt_) && any(bayestopt_.pshape==0) && any(bayestopt_.pshape~=0)
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@ -373,52 +373,52 @@ options_mom_.mom.index.E_y_pos = zeros(options_mom_.obs_nbr,1);
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options_mom_.mom.index.E_yy_pos = zeros(options_mom_.obs_nbr,options_mom_.obs_nbr); %position in matched moments block
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options_mom_.mom.index.E_yyt_pos = zeros(options_mom_.obs_nbr,options_mom_.obs_nbr,0); %position in matched moments block
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for jm=1:size(matched_moments_,1)
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for jm=1:size(M_.matched_moments,1)
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% higher-order product moments not supported yet for GMM
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if strcmp(options_mom_.mom.mom_method, 'GMM') && sum(matched_moments_{jm,3}) > 2
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if strcmp(options_mom_.mom.mom_method, 'GMM') && sum(M_.matched_moments{jm,3}) > 2
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error('method_of_moments: GMM does not yet support product moments higher than 2. Change row %d in ''matched_moments'' block.',jm);
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end
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% Check if declared variables are also observed (needed as otherwise the dataset variables won't coincide)
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if any(~ismember(oo_.dr.inv_order_var(matched_moments_{jm,1})', oo_.dr.obs_var))
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if any(~ismember(oo_.dr.inv_order_var(M_.matched_moments{jm,1})', oo_.dr.obs_var))
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error('method_of_moments: Variables in row %d in ''matched_moments'' block need to be declared as VAROBS.', jm)
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end
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if strcmp(options_mom_.mom.mom_method, 'GMM')
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% Check (for now) that only lags are declared
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if any(matched_moments_{jm,2}>0)
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if any(M_.matched_moments{jm,2}>0)
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error('method_of_moments: Leads in row %d in the ''matched_moments'' block are not supported for GMM, shift the moments and declare only lags.', jm)
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end
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% Check (for now) that first declared variable has zero lag
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if matched_moments_{jm,2}(1)~=0
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if M_.matched_moments{jm,2}(1)~=0
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error('method_of_moments: The first variable declared in row %d in the ''matched_moments'' block is not allowed to have a lead or lag for GMM;\n reorder the variables in the row such that the first variable has zero lag!',jm)
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end
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end
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vars = oo_.dr.inv_order_var(matched_moments_{jm,1})';
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if sum(matched_moments_{jm,3}) == 1
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vars = oo_.dr.inv_order_var(M_.matched_moments{jm,1})';
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if sum(M_.matched_moments{jm,3}) == 1
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% First-order product moment
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vpos = (oo_.dr.obs_var == vars);
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options_mom_.mom.index.E_y(vpos,1) = true;
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options_mom_.mom.index.E_y_pos(vpos,1) = jm;
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matched_moments_{jm,4}=['E(',M_.endo_names{matched_moments_{jm,1}},')'];
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matched_moments_{jm,5}=['$E(',M_.endo_names_tex{matched_moments_{jm,1}},')$'];
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elseif sum(matched_moments_{jm,3}) == 2
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M_.matched_moments{jm,4}=['E(',M_.endo_names{M_.matched_moments{jm,1}},')'];
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M_.matched_moments{jm,5}=['$E(',M_.endo_names_tex{M_.matched_moments{jm,1}},')$'];
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elseif sum(M_.matched_moments{jm,3}) == 2
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% Second-order product moment
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idx1 = (oo_.dr.obs_var == vars(1));
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idx2 = (oo_.dr.obs_var == vars(2));
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lag1 = matched_moments_{jm,2}(1);
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lag2 = matched_moments_{jm,2}(2);
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lag1 = M_.matched_moments{jm,2}(1);
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lag2 = M_.matched_moments{jm,2}(2);
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if lag1==0 && lag2==0 % contemporaneous covariance matrix
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options_mom_.mom.index.E_yy(idx1,idx2) = true;
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options_mom_.mom.index.E_yy(idx2,idx1) = true;
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options_mom_.mom.index.E_yy_pos(idx1,idx2) = jm;
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options_mom_.mom.index.E_yy_pos(idx2,idx1) = jm;
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matched_moments_{jm,4}=['E(',M_.endo_names{matched_moments_{jm,1}(1)},',',M_.endo_names{matched_moments_{jm,1}(2)},')'];
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matched_moments_{jm,5}=['$E({',M_.endo_names_tex{matched_moments_{jm,1}(1)},'}_t,{',M_.endo_names_tex{matched_moments_{jm,1}(1)},'}_t)$'];
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M_.matched_moments{jm,4}=['E(',M_.endo_names{M_.matched_moments{jm,1}(1)},',',M_.endo_names{M_.matched_moments{jm,1}(2)},')'];
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M_.matched_moments{jm,5}=['$E({',M_.endo_names_tex{M_.matched_moments{jm,1}(1)},'}_t,{',M_.endo_names_tex{M_.matched_moments{jm,1}(1)},'}_t)$'];
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elseif lag1==0 && lag2 < 0
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options_mom_.mom.index.E_yyt(idx1,idx2,-lag2) = true;
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options_mom_.mom.index.E_yyt_pos(idx1,idx2,-lag2) = jm;
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matched_moments_{jm,4}=['E(',M_.endo_names{matched_moments_{jm,1}(1)},',',M_.endo_names{matched_moments_{jm,1}(2)},'(',num2str(lag2),'))'];
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matched_moments_{jm,5}=['$E({',M_.endo_names_tex{matched_moments_{jm,1}(1)},'}_t\times{',M_.endo_names_tex{matched_moments_{jm,1}(1)},'_{t',num2str(lag2) ,'})$'];
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M_.matched_moments{jm,4}=['E(',M_.endo_names{M_.matched_moments{jm,1}(1)},',',M_.endo_names{M_.matched_moments{jm,1}(2)},'(',num2str(lag2),'))'];
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M_.matched_moments{jm,5}=['$E({',M_.endo_names_tex{M_.matched_moments{jm,1}(1)},'}_t\times{',M_.endo_names_tex{M_.matched_moments{jm,1}(1)},'_{t',num2str(lag2) ,'})$'];
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end
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end
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end
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@ -427,27 +427,27 @@ end
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% @wmutschl: add check for duplicate moments by using the cellfun and unique functions
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%Remove duplicate elements
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UniqueMomIdx = [nonzeros(options_mom_.mom.index.E_y_pos); nonzeros(tril(options_mom_.mom.index.E_yy_pos)); nonzeros(options_mom_.mom.index.E_yyt_pos)];
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DuplicateMoms = setdiff(1:size(matched_moments_,1),UniqueMomIdx);
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DuplicateMoms = setdiff(1:size(M_.matched_moments,1),UniqueMomIdx);
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if ~isempty(DuplicateMoms)
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fprintf('Found and removed duplicate declared moments in ''matched_moments'' block in rows: %s.\n',num2str(DuplicateMoms))
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end
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%reorder matched_moments_ to be compatible with options_mom_.mom.index
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matched_moments_ = matched_moments_(UniqueMomIdx,:);
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%reorder M_.matched_moments to be compatible with options_mom_.mom.index
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M_.matched_moments = M_.matched_moments(UniqueMomIdx,:);
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if strcmp(options_mom_.mom.mom_method,'SMM')
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options_mom_.mom=rmfield(options_mom_.mom,'index');
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end
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% Check if both prefilter and first moments were specified
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options_mom_.mom.first_moment_indicator = find(cellfun(@(x) sum(abs(x))==1,matched_moments_(:,3)))';
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options_mom_.mom.first_moment_indicator = find(cellfun(@(x) sum(abs(x))==1,M_.matched_moments(:,3)))';
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if options_mom_.prefilter && ~isempty(options_mom_.mom.first_moment_indicator)
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fprintf('Centered moments requested (prefilter option is set); therefore, ignore declared first moments in ''matched_moments'' block in rows: %u.\n',options_mom_.mom.first_moment_indicator');
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matched_moments_(options_mom_.mom.first_moment_indicator,:)=[]; %remove first moments entries
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M_.matched_moments(options_mom_.mom.first_moment_indicator,:)=[]; %remove first moments entries
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options_mom_.mom.first_moment_indicator = [];
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end
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options_mom_.mom.mom_nbr = size(matched_moments_,1);
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options_mom_.mom.mom_nbr = size(M_.matched_moments,1);
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% Get maximum lag number for autocovariances/autocorrelations
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options_mom_.ar = max(cellfun(@max,matched_moments_(:,2))) - min(cellfun(@min,matched_moments_(:,2)));
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options_mom_.ar = max(cellfun(@max,M_.matched_moments(:,2))) - min(cellfun(@min,M_.matched_moments(:,2)));
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% -------------------------------------------------------------------------
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% Step 3: Checks and transformations for estimated parameters, priors, and bounds
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@ -625,7 +625,7 @@ if options_mom_.ar > options_mom_.nobs+1
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end
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% Get data moments for the method of moments
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[oo_.mom.data_moments, oo_.mom.m_data] = method_of_moments_data_moments(dataset_.data, oo_, matched_moments_, options_mom_);
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[oo_.mom.data_moments, oo_.mom.m_data] = method_of_moments_data_moments(dataset_.data, oo_, M_.matched_moments, options_mom_);
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% Get shock series for SMM and set variance correction factor
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if strcmp(options_mom_.mom.mom_method,'SMM')
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@ -702,7 +702,7 @@ try
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% at initial parameters and identity weighting matrix
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oo_.mom.Sw = eye(options_mom_.mom.mom_nbr);
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tic_id = tic;
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[fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
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[fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, M_, options_mom_);
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elapsed_time = toc(tic_id);
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if isnan(fval)
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error('method_of_moments: The initial value of the objective function is NaN')
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@ -840,7 +840,7 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
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options_mom_.vector_output = false;
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end
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[xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, optimizer_vec(optim_iter), options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],...
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Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
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Bounds, oo_, estim_params_, M_, options_mom_);
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if options_mom_.vector_output
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fval = fval'*fval;
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end
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@ -853,9 +853,9 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
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options_mom_.vector_output = false;
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% Update M_ and DynareResults (in particular to get oo_.mom.model_moments)
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M_ = set_all_parameters(xparam1,estim_params_,M_);
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[fval, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
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[fval, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
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% Compute Standard errors
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SE = method_of_moments_standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_, Woptflag);
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SE = method_of_moments_standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
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% Store results in output structure
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oo_.mom = display_estimation_results_table(xparam1,SE,M_,options_mom_,estim_params_,bayestopt_laplace,oo_.mom,prior_dist_names,sprintf('%s (STAGE %u)',options_mom_.mom.mom_method,stage_iter),sprintf('%s_stage_%u',lower(options_mom_.mom.mom_method),stage_iter));
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@ -870,7 +870,7 @@ if options_mom_.mom.mom_nbr > length(xparam1)
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W_opt = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
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oo_j=oo_;
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oo_j.mom.Sw = chol(W_opt);
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[fval] = feval(objective_function, xparam1, Bounds, oo_j, estim_params_, matched_moments_, M_, options_mom_);
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[fval] = feval(objective_function, xparam1, Bounds, oo_j, estim_params_, M_, options_mom_);
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end
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% Compute J statistic
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@ -891,18 +891,18 @@ end
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% -------------------------------------------------------------------------
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title = ['Data moments and model moments (',options_mom_.mom.mom_method,')'];
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headers = {'Moment','Data','Model','% dev. target'};
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labels= matched_moments_(:,4);
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labels= M_.matched_moments(:,4);
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data_mat=[oo_.mom.data_moments oo_.mom.model_moments 100*abs((oo_.mom.model_moments-oo_.mom.data_moments)./oo_.mom.data_moments)];
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dyntable(options_mom_, title, headers, labels, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
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if options_mom_.TeX
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lh = cellofchararraymaxlength(labels)+2;
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labels_TeX = matched_moments_(:,5);
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labels_TeX = M_.matched_moments(:,5);
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dyn_latex_table(M_, options_mom_, title, 'sim_corr_matrix', headers, labels_TeX, data_mat, lh, 10, 7);
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end
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if options_mom_.mode_check.status
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method_of_moments_mode_check(objective_function,xparam1,SE,options_mom_,M_,estim_params_,Bounds,bayestopt_laplace,...
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Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_)
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Bounds, oo_, estim_params_, M_, options_mom_)
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end
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fprintf('\n==== Method of Moments Estimation (%s) Completed ====\n\n',options_mom_.mom.mom_method)
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@ -1,5 +1,5 @@
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function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_)
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% [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_)
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function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
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% [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
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% -------------------------------------------------------------------------
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% This function evaluates the objective function for GMM/SMM estimation
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% =========================================================================
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@ -8,7 +8,6 @@ function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_o
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% o Bounds: structure containing parameter bounds
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% o oo_: structure for results
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% o estim_params_: structure describing the estimated_parameters
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% o matched_moments_: structure containing information about selected moments to match in estimation
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% o M_ structure describing the model
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% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
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% -------------------------------------------------------------------------
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@ -186,7 +185,7 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
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if options_mom_.prefilter
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y_sim = bsxfun(@minus, y_sim, mean(y_sim,1));
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end
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oo_.mom.model_moments = method_of_moments_data_moments(y_sim, oo_, matched_moments_, options_mom_);
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oo_.mom.model_moments = method_of_moments_data_moments(y_sim, oo_, M_.matched_moments, options_mom_);
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end
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@ -1,5 +1,5 @@
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function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_, Wopt_flag)
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% [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_, Wopt_flag)
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function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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% [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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% -------------------------------------------------------------------------
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% This function computes standard errors to the method of moments estimates
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% Adapted from replication codes of
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@ -11,7 +11,6 @@ function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, obj
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% o Bounds: structure containing parameter bounds
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% o oo_: structure for results
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% o estim_params_: structure describing the estimated_parameters
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% o matched_moments_: structure containing information about selected moments to match in estimation
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% o M_ structure describing the model
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% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
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% o Wopt_flag: indicator whether the optimal weighting is actually used
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@ -62,12 +61,12 @@ for i=1:dim_params
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%Positive step
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xparam_eps_p = xparam;
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xparam_eps_p(i,1) = xparam_eps_p(i) + eps_value;
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[~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
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[~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, Bounds, oo_, estim_params_, M_, options_mom_);
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% Negative step
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xparam_eps_m = xparam;
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xparam_eps_m(i,1) = xparam_eps_m(i) - eps_value;
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[~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
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[~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, Bounds, oo_, estim_params_, M_, options_mom_);
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% The Jacobian:
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if nnz(info_p)==0 && nnz(info_m)==0
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|||
|
|
@ -148,7 +148,23 @@ pause(1);
|
|||
%--------------------------------------------------------------------------
|
||||
% Method of Moments Estimation
|
||||
%--------------------------------------------------------------------------
|
||||
% matched_moments blocks : We don't have an interface yet
|
||||
matched_moments;
|
||||
YGR;
|
||||
INFL;
|
||||
INT;
|
||||
%second-order contemporenous product moments
|
||||
YGR*YGR;
|
||||
YGR*INFL;
|
||||
YGR*INT;
|
||||
INFL*INFL;
|
||||
INFL*INT;
|
||||
INT*INT;
|
||||
%second-order temporal product moments
|
||||
YGR*YGR(-1);
|
||||
INT*INT(-1);
|
||||
INFL*INFL(-1);
|
||||
end;
|
||||
|
||||
% get indices in declaration order
|
||||
iYGR = strmatch('YGR', M_.endo_names,'exact');
|
||||
iINFL = strmatch('INFL', M_.endo_names,'exact');
|
||||
|
|
@ -181,6 +197,9 @@ matched_moments_ = {
|
|||
[iINFL iINFL] [0 -1], [1 1];
|
||||
};
|
||||
|
||||
if ~isequal(M_.matched_moments,matched_moments_)
|
||||
error('Translation to matched_moments-block failed')
|
||||
end
|
||||
|
||||
@#for mommethod in ["GMM", "SMM"]
|
||||
method_of_moments(
|
||||
|
|
|
|||
|
|
@ -82,10 +82,32 @@ end;
|
|||
estimated_params_init(use_calibration);
|
||||
end;
|
||||
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Method of Moments Estimation
|
||||
%--------------------------------------------------------------------------
|
||||
% matched_moments blocks : We don't have an interface yet
|
||||
matched_moments;
|
||||
c;
|
||||
n;
|
||||
iv;
|
||||
c*c;
|
||||
c*iv;
|
||||
iv*n;
|
||||
iv*iv;
|
||||
n*c;
|
||||
n*n;
|
||||
c*c(-1);
|
||||
n*n(-1);
|
||||
iv*iv(-1);
|
||||
|
||||
c*c(-3);
|
||||
n*n(-3);
|
||||
iv*iv(-3);
|
||||
|
||||
c*c(-5);
|
||||
n*n(-5);
|
||||
iv*iv(-5);
|
||||
end;
|
||||
|
||||
% get indices in declaration order
|
||||
ic = strmatch('c', M_.endo_names,'exact');
|
||||
|
|
@ -104,9 +126,9 @@ matched_moments_ = {
|
|||
[ic iiv] [0 0], [1 1];
|
||||
%[ic in ] [0 0], [1 1];
|
||||
%[iiv ic ] [0 0], [1 1];
|
||||
[iiv in ] [0 0], [1 1];
|
||||
[in iiv] [0 0], [1 1];
|
||||
[iiv iiv] [0 0], [1 1];
|
||||
[in ic ] [0 0], [1 1];
|
||||
[ic in] [0 0], [1 1];
|
||||
%[in iiv] [0 0], [1 1];
|
||||
[in in ] [0 0], [1 1];
|
||||
|
||||
|
|
@ -124,6 +146,9 @@ matched_moments_ = {
|
|||
|
||||
};
|
||||
|
||||
if ~isequal(M_.matched_moments,matched_moments_)
|
||||
error('Translation to matched_moments-block failed')
|
||||
end
|
||||
|
||||
|
||||
method_of_moments(
|
||||
|
|
@ -133,7 +158,7 @@ matched_moments_ = {
|
|||
|
||||
% Options for both GMM and SMM
|
||||
%, bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
|
||||
, order = 2 % order of Taylor approximation in perturbation
|
||||
, order = @{orderApp} % order of Taylor approximation in perturbation
|
||||
%, penalized_estimator % use penalized optimization
|
||||
%, pruning % use pruned state space system at higher-order
|
||||
%, verbose % display and store intermediate estimation results
|
||||
|
|
|
|||
|
|
@ -86,6 +86,23 @@ pause(1);
|
|||
% Method of Moments Estimation
|
||||
%--------------------------------------------------------------------------
|
||||
% matched_moments blocks : We don't have an interface yet
|
||||
matched_moments;
|
||||
c;
|
||||
n;
|
||||
iv;
|
||||
|
||||
c*c;
|
||||
c*iv;
|
||||
c*n;
|
||||
iv*c;
|
||||
iv*iv;
|
||||
iv*n;
|
||||
n*n;
|
||||
|
||||
c*c(-1);
|
||||
n*n(-1);
|
||||
iv*iv(-1);
|
||||
end;
|
||||
|
||||
% get indices in declaration order
|
||||
ic = strmatch('c', M_.endo_names,'exact');
|
||||
|
|
@ -102,9 +119,9 @@ matched_moments_ = {
|
|||
[ic ic ] [0 0], [1 1];
|
||||
[ic iiv] [0 0], [1 1];
|
||||
[ic in ] [0 0], [1 1];
|
||||
[iiv ic ] [0 0], [1 1];
|
||||
[ic iiv] [0 0], [1 1];
|
||||
[iiv iiv] [0 0], [1 1];
|
||||
[iiv in ] [0 0], [1 1];
|
||||
[in iiv] [0 0], [1 1];
|
||||
% [in ic ] [0 0], [1 1];
|
||||
% [in iiv] [0 0], [1 1];
|
||||
[in in ] [0 0], [1 1];
|
||||
|
|
@ -114,7 +131,9 @@ matched_moments_ = {
|
|||
% [iiv iiv] [0 -1], [1 1];
|
||||
};
|
||||
|
||||
|
||||
if ~isequal(M_.matched_moments,matched_moments_)
|
||||
error('Translation to matched_moments-block failed')
|
||||
end
|
||||
|
||||
@#for mommethod in ["SMM"]
|
||||
method_of_moments(
|
||||
|
|
|
|||
|
|
@ -58,7 +58,24 @@ end;
|
|||
%--------------------------------------------------------------------------
|
||||
% Method of Moments Estimation
|
||||
%--------------------------------------------------------------------------
|
||||
% matched_moments blocks : We don't have an interface yet
|
||||
|
||||
matched_moments;
|
||||
c;
|
||||
n;
|
||||
iv;
|
||||
|
||||
c*c;
|
||||
c*iv;
|
||||
c*n;
|
||||
iv*c;
|
||||
iv*iv;
|
||||
iv*n;
|
||||
n*n;
|
||||
|
||||
c*c(-1);
|
||||
n*n(-1);
|
||||
iv*iv(-1);
|
||||
end;
|
||||
|
||||
% get indices in declaration order
|
||||
ic = strmatch('c', M_.endo_names,'exact');
|
||||
|
|
@ -75,18 +92,19 @@ matched_moments_ = {
|
|||
[ic ic ] [0 0], [1 1];
|
||||
[ic iiv] [0 0], [1 1];
|
||||
[ic in ] [0 0], [1 1];
|
||||
[iiv ic ] [0 0], [1 1];
|
||||
[ic iiv] [0 0], [1 1];
|
||||
[iiv iiv] [0 0], [1 1];
|
||||
[iiv in ] [0 0], [1 1];
|
||||
% [in ic ] [0 0], [1 1];
|
||||
% [in iiv] [0 0], [1 1];
|
||||
[in iiv] [0 0], [1 1];
|
||||
[in in ] [0 0], [1 1];
|
||||
[ic ic ] [0 -1], [1 1];
|
||||
[in in ] [0 -1], [1 1];
|
||||
[iiv iiv] [0 -1], [1 1];
|
||||
% [iiv iiv] [0 -1], [1 1];
|
||||
};
|
||||
|
||||
if ~isequal(M_.matched_moments,matched_moments_)
|
||||
error('Translation to matched_moments-block failed')
|
||||
end
|
||||
|
||||
weighting_matrix=diag([1000;ones(8,1)]);
|
||||
save('test_matrix.mat','weighting_matrix')
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue