diff --git a/matlab/+mom/check_irf_matching_file.m b/matlab/+mom/check_irf_matching_file.m
index 70eb5b37a662cebe94ed8f25c969caab77a28e51..3aa5db2726446db02de2cb6b6686b08a9806d208 100644
--- a/matlab/+mom/check_irf_matching_file.m
+++ b/matlab/+mom/check_irf_matching_file.m
@@ -6,7 +6,7 @@ function [irf_matching_file_name, irf_matching_file_path] = check_irf_matching_f
% -------------------------------------------------------------------------
% INPUTS
% - irf_matching_file: [string] user provided name (with possible path and extension)
-% of the MATLAB function that transforms model irfs
+% of the MATLAB function that transforms model IRFs
% -------------------------------------------------------------------------
% OUTPUTS
% - irf_matching_file_name: [string] name of the MATLAB function (without extension)
diff --git a/matlab/+mom/default_option_mom_values.m b/matlab/+mom/default_option_mom_values.m
index 6504af5666c972f1c0a1c86e3584d9ff5ced4ffb..5ce9d34a94e491d76a27b9fb754a4d2c6dccf850 100644
--- a/matlab/+mom/default_option_mom_values.m
+++ b/matlab/+mom/default_option_mom_values.m
@@ -9,8 +9,8 @@ function options_mom_ = default_option_mom_values(options_mom_, options_, dname,
% control of the estimation instead of possibly having to deal with options
% chosen somewhere else in the mod file.
% Note 2: we call a "mode" the minimum of the objective function, i.e.
-% the parameter vector that minimizes the distance between the moments/irfs
-% computed from the model and the moments/irfs computed from the data.
+% the parameter vector that minimizes the distance between the moments/IRFs
+% computed from the model and the moments/IRFs computed from the data.
% -------------------------------------------------------------------------
% INPUTS
% o options_mom_: [structure] all user-specified settings (from the method_of_moments command)
diff --git a/matlab/+mom/display_comparison_moments_irfs.m b/matlab/+mom/display_comparison_moments_irfs.m
index cdefd80c7f7c8520c7d4d228c377795c21a45322..da31a4e34a0fafb19826575b1f8a279062782a6a 100644
--- a/matlab/+mom/display_comparison_moments_irfs.m
+++ b/matlab/+mom/display_comparison_moments_irfs.m
@@ -1,7 +1,7 @@
function display_comparison_moments_irfs(M_, options_mom_, data_moments, model_moments)
% display_comparison_moments_irfs(M_, options_mom_, data_moments, model_moments)
% -------------------------------------------------------------------------
-% Displays and saves to disk the comparison of the data moments/irfs and the model moments/irfs
+% Displays and saves to disk the comparison of the data moments/IRFs and the model moments/IRFs
% -------------------------------------------------------------------------
% INPUTS
% M_: [structure] model information
@@ -40,7 +40,7 @@ function display_comparison_moments_irfs(M_, options_mom_, data_moments, model_m
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
- titl = upper('Comparison of matched data irfs and model irfs');
+ titl = upper('Comparison of matched data IRFs and model IRFs');
headers = {'IRF','Data','Model'};
for jm = 1:size(M_.matched_irfs,1)
labels{jm,1} = [M_.endo_names{M_.matched_irfs{jm,1}(1)} ' ' M_.exo_names{M_.matched_irfs{jm,1}(2)} ' (' num2str(M_.matched_irfs{jm,1}(3)) ')'];
diff --git a/matlab/+mom/graph_comparison_irfs.m b/matlab/+mom/graph_comparison_irfs.m
index 54aab693815cdc09c0db65df6aabcc208715f7bf..ee6d09e73bd09b1a1cc8a361cdff73b2b0f985ce 100644
--- a/matlab/+mom/graph_comparison_irfs.m
+++ b/matlab/+mom/graph_comparison_irfs.m
@@ -1,14 +1,14 @@
function graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
% graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
% -------------------------------------------------------------------------
-% Plots and saves to disk the comparison of the selected data irfs and corresponding model irfs
+% Plots and saves to disk the comparison of the selected data IRFs and corresponding model IRfs
% -------------------------------------------------------------------------
% INPUTS
-% matched_irfs: [matrix] information on matched data irfs
-% irf_model_varobs: [matrix] model irfs for observable variables
+% matched_irfs: [matrix] information on matched data IRFs
+% irf_model_varobs: [matrix] model IRFs for observable variables
% varobs_id: [vector] index for observable variables in endo_names
-% irf_horizon: [scalar] maximum horizon of irfs
-% relative_irf: [boolean] if true, plots normalized irfs
+% irf_horizon: [scalar] maximum horizon of IRFs
+% relative_irf: [boolean] if true, plots normalized IRFs
% endo_names: [cell] names of endogenous variables
% exo_names: [cell] names of exogenous variables
% exo_names_tex: [cell] names of exogenous variables in latex
diff --git a/matlab/+mom/matched_irfs_blocks.m b/matlab/+mom/matched_irfs_blocks.m
index 0f91eed6ed7f92a03cef90722dd354dc4cb215c2..cce2d827816f5b17d1753072eb68aa0c223e078c 100644
--- a/matlab/+mom/matched_irfs_blocks.m
+++ b/matlab/+mom/matched_irfs_blocks.m
@@ -13,9 +13,9 @@ function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_bloc
% endo_names: [cell array] list of endogenous variables
% -------------------------------------------------------------------------
% OUTPUT
-% data_irfs: [matrix] irfs for VAROBS as declared in matched_irfs block
-% weight_mat: [matrix] weighting matrix for irfs as declared in matched_irfs_weight block
-% irf_index: [vector] index for selecting specific irfs from full IRF matrix of observables
+% data_irfs: [matrix] IRFs for VAROBS as declared in matched_irfs block
+% weight_mat: [matrix] weighting matrix for IRFs as declared in matched_irfs_weight block
+% irf_index: [vector] index for selecting specific IRFs from full IRF matrix of observables
% max_irf_horizon: [scalar] maximum IRF horizon as declared in matched_irfs block
% -------------------------------------------------------------------------
% This function is called by
@@ -42,8 +42,8 @@ function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_bloc
max_irf_horizon = max(cellfun(@(x) x(end), matched_irfs(:,1))); % get maximum IRF horizon
% create full matrix where 1st dimension are IRF periods, 2nd dimension are variables as declared in VAROBS, 3rd dimension are shocks.
-data_irfs = nan(max_irf_horizon,obs_nbr,exo_nbr);
-% overwrite nan values if they are declared in matched_irfs block; remaining nan values will be later ignored in the matching
+data_irfs = NaN(max_irf_horizon,obs_nbr,exo_nbr);
+% overwrite NaN values if they are declared in matched_irfs block; remaining NaN values will be later ignored in the matching
for jj = 1:size(matched_irfs,1)
id_var = matched_irfs{jj,1}(1);
id_varobs = find(varobs_id==id_var,1);
@@ -57,7 +57,7 @@ for jj = 1:size(matched_irfs,1)
data_irfs(id_irf_period,id_varobs,id_shock) = irf_value;
end
% create (full) empirical weighting matrix
-weight_mat = eye(max_irf_horizon*obs_nbr*exo_nbr); % identity matrix by default: all irfs are equally important
+weight_mat = eye(max_irf_horizon*obs_nbr*exo_nbr); % identity matrix by default: all IRFs are equally important
for jj = 1:size(matched_irfs_weight,1)
id_var1 = matched_irfs_weight{jj,1}(1); id_varobs1 = find(varobs_id==id_var1,1); id_shock1 = matched_irfs_weight{jj,1}(2); id_irf_period1 = matched_irfs_weight{jj,1}(3);
id_var2 = matched_irfs_weight{jj,2}(1); id_varobs2 = find(varobs_id==id_var2,1); id_shock2 = matched_irfs_weight{jj,2}(2); id_irf_period2 = matched_irfs_weight{jj,2}(3);
@@ -75,7 +75,7 @@ for jj = 1:size(matched_irfs_weight,1)
weight_mat(idweight_mat1,idweight_mat2) = weight_mat_value;
weight_mat(idweight_mat2,idweight_mat1) = weight_mat_value; % symmetry
end
-% focus only on specified irfs
+% focus only on specified IRFs
irf_index = find(~isnan(data_irfs));
data_irfs = data_irfs(irf_index);
weight_mat = weight_mat(irf_index,irf_index);
\ No newline at end of file
diff --git a/matlab/+mom/mode_compute_irf_matching.m b/matlab/+mom/mode_compute_irf_matching.m
index 4ae7de813c2c3ff49694967831e672baee85bae6..d7673b1261e98556933a4f29eff217a0e8555dbf 100644
--- a/matlab/+mom/mode_compute_irf_matching.m
+++ b/matlab/+mom/mode_compute_irf_matching.m
@@ -1,11 +1,11 @@
function [xparam1, hessian_xparam1, fval, mom_verbose] = mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, doBayesianEstimation, weighting_info, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [xparam1, hessian_xparam1, fval, mom_verbose] = mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, doBayesianEstimation, weighting_info, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% -------------------------------------------------------------------------
-% Computes the minimum of the objective function (distance between data irfs
-% and model irfs) for a sequence of optimizers.
+% Computes the minimum of the objective function (distance between data IRFs
+% and model IRFs) for a sequence of optimizers.
% Note that we call a "mode" the minimum of the objective function, i.e.
-% the parameter vector that minimizes the distance between the irfs
-% computed from the model and the irfs computed from the data.
+% the parameter vector that minimizes the distance between the IRFs
+% computed from the model and the IRFs computed from the data.
% -------------------------------------------------------------------------
% INPUTS
% xparam0: [vector] initialized parameters
diff --git a/matlab/+mom/objective_function.m b/matlab/+mom/objective_function.m
index bbef5e322f66934bc8d87cb4823532503bfa9216..fdcbd2365ee22d80c431346a020a90148f242e43 100644
--- a/matlab/+mom/objective_function.m
+++ b/matlab/+mom/objective_function.m
@@ -2,11 +2,11 @@ function [fval, info, exit_flag, df, junk_hessian, Q, model_moments, model_momen
% [fval, info, exit_flag, df, junk_hessian, 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)
% -------------------------------------------------------------------------
% This function evaluates the objective function for method of moments estimation,
-% i.e. distance between model and data moments/irfs, possibly augmented with priors.
+% i.e. distance between model and data moments/IRFs, possibly augmented with priors.
% -------------------------------------------------------------------------
% INPUTS (same ones as in dsge_likelihood.m and dsge_var_likelihood.m)
% - xparam: [vector] current value of estimated parameters as returned by set_prior()
-% - data_moments: [vector] data with moments/irfs to match (corresponds to dataset_ in likelihood-based estimation)
+% - data_moments: [vector] data with moments/IRFs to match (corresponds to dataset_ in likelihood-based estimation)
% - weighting_info: [structure] storing information on weighting matrices (corresponds to dataset_info in likelihood-based estimation)
% - options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% - M_ [structure] model information
@@ -27,7 +27,7 @@ function [fval, info, exit_flag, df, junk_hessian, Q, model_moments, model_momen
% - Q: [double] value of the quadratic form of the moment difference
% - model_moments: [vector] model moments
% - model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
-% - irf_model_varobs: [matrix] model irfs for observable variables (used for plotting matched irfs in mom.run)
+% - irf_model_varobs: [matrix] model IRFs for observable variables (used for plotting matched IRfs in mom.run)
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
@@ -78,12 +78,12 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
if options_mom_.mom.vector_output == 1
if options_mom_.mom.penalized_estimator
- df = nan(options_mom_.mom.mom_nbr+length(xparam),length(xparam));
+ df = NaN(options_mom_.mom.mom_nbr+length(xparam),length(xparam));
else
- df = nan(options_mom_.mom.mom_nbr,length(xparam));
+ df = NaN(options_mom_.mom.mom_nbr,length(xparam));
end
else
- df = nan(length(xparam),1);
+ df = NaN(length(xparam),1);
end
end
end
@@ -226,7 +226,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
scaled_shock_series(:,i_exo_var) = options_mom_.mom.shock_series(:,i_exo_var)*chol_S; % set non-zero entries
% simulate series
y_sim = simult_(M_, options_mom_, dr.ys, dr, scaled_shock_series, options_mom_.order);
- % provide meaningful penalty if data is nan or inf
+ % provide meaningful penalty if data is NaN or Inf
if any(any(isnan(y_sim))) || any(any(isinf(y_sim)))
info(1)=180;
info(4) = 0.1;
@@ -255,14 +255,14 @@ end
%------------------------------------------------------------------------------
-% IRF_MATCHING using STOCH_SIMUL: Compute irfs given model solution and Cholesky
+% IRF_MATCHING using STOCH_SIMUL: Compute IRFs given model solution and Cholesky
% decomposition on shock covariance matrix; this resembles the core codes in
% stoch_simul
%------------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING') && strcmp(options_mom_.mom.simulation_method,'STOCH_SIMUL')
cs = get_lower_cholesky_covariance(M_.Sigma_e,options_mom_.add_tiny_number_to_cholesky);
irf_shocks_indx = getIrfShocksIndx(M_, options_mom_);
- model_irf = nan(options_mom_.irf,M_.endo_nbr,M_.exo_nbr);
+ model_irf = NaN(options_mom_.irf,M_.endo_nbr,M_.exo_nbr);
for i = irf_shocks_indx
if options_mom_.order>1 && options_mom_.relative_irf % normalize shock to 0.01 before IRF generation for GIRFs; multiply with 100 later
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);
@@ -287,7 +287,7 @@ if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING') && strcmp(options_mom_.mom
end
model_irf(:,:,i) = transpose(y_irf);
end
- % do transformations on model irfs if irf_matching_file is provided
+ % do transformations on model IRFs if irf_matching_file is provided
if ~isempty(options_mom_.mom.irf_matching_file.name)
[model_irf, check] = feval(str2func(options_mom_.mom.irf_matching_file.name), model_irf, M_, options_mom_, dr.ys);
if check
diff --git a/matlab/+mom/print_info_on_estimation_settings.m b/matlab/+mom/print_info_on_estimation_settings.m
index 5d6ad6bfff8596d5cdc83acf3e10806e80742591..4085f1e0f1344f1b55aff9fe18ef7071b451d583 100644
--- a/matlab/+mom/print_info_on_estimation_settings.m
+++ b/matlab/+mom/print_info_on_estimation_settings.m
@@ -131,7 +131,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
end
fprintf('\n - number of matched moments: %d', options_mom_.mom.mom_nbr);
elseif strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
- fprintf('\n - number of matched irfs: %d', options_mom_.mom.mom_nbr);
+ fprintf('\n - number of matched IRFs: %d', options_mom_.mom.mom_nbr);
end
fprintf('\n - number of parameters: %d', number_of_estimated_parameters);
fprintf('\n\n');
\ No newline at end of file
diff --git a/matlab/+mom/run.m b/matlab/+mom/run.m
index fb1677931c5b17f69a86042f4ed4bea27d56030b..e970ccd278df04132573ec93e323377656b813d5 100644
--- a/matlab/+mom/run.m
+++ b/matlab/+mom/run.m
@@ -17,12 +17,12 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% o Bayesian MCMC estimation
% o Display of results
% - GMM/SMM: J-Test and fit of moments
-% - IRF_MATCHING: fit of irfs
+% - IRF_MATCHING: fit of IRFs
% o Clean up
% -------------------------------------------------------------------------
% Note that we call a "mode" the minimum of the objective function, i.e.
-% the parameter vector that minimizes the distance between the moments/irfs
-% computed from the model and the moments/irfs computed from the data.
+% the parameter vector that minimizes the distance between the moments/IRFs
+% computed from the model and the moments/IRFs computed from the data.
% -------------------------------------------------------------------------
% This function is inspired by replication codes accompanied to the following papers:
% GMM/SMM:
@@ -45,7 +45,7 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% vars: matched_moments{:,1});
% lead/lags: matched_moments{:,2};
% powers: matched_moments{:,3};
-% o matched_irfs: [cell] information about selected irfs to match in IRF_MATCHING estimation
+% o matched_irfs: [cell] information about selected IRFs to match in IRF_MATCHING estimation
% o matched_irfs_weights: [cell] information about entries in weight matrix for an IRF_MATCHING estimation
% o options_mom_: [structure] information about settings specified by the user
% -------------------------------------------------------------------------
@@ -238,16 +238,16 @@ if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
error('method_of_moments: Something wrong while computing inv(W), check your weighting matrix!');
end
if any(isnan(oo_.mom.weighting_info.Winv(:))) || any(isinf(oo_.mom.weighting_info.Winv(:)))
- error('method_of_moments: There are nan or inf in inv(W), check your weighting matrix!');
+ error('method_of_moments: There are NaN or Inf values in inv(W), check your weighting matrix!');
end
- % compute log determinant of inverse of weighting matrix in a robust way to avoid inf/nan
+ % compute log determinant of inverse of weighting matrix in a robust way to avoid Inf or NaN
try
oo_.mom.weighting_info.Winv_logdet = 2*sum(log(diag(chol(oo_.mom.weighting_info.Winv))));
catch
error('method_of_moments: Something wrong while computing log(det(inv(W))), check your weighting matrix!');
end
if any(isnan(oo_.mom.weighting_info.Winv_logdet(:))) || any(isinf(oo_.mom.weighting_info.Winv_logdet(:)))
- error('method_of_moments: There are nan or inf in log(det(inv(W))), check your weighting matrix!');
+ error('method_of_moments: There are NaN or Inf values in log(det(inv(W))), check your weighting matrix!');
end
options_mom_.mom.mom_nbr = length(options_mom_.mom.irfIndex);
end
@@ -606,12 +606,12 @@ else
end
else
variances = bayestopt_.p2.*bayestopt_.p2;
- id_inf = isinf(variances);
- variances(id_inf) = 1;
+ id_Inf = isinf(variances);
+ variances(id_Inf) = 1;
invhess = options_mom_.mh_posterior_mode_estimation*diag(variances);
xparam1 = bayestopt_.p5;
- id_nan = isnan(xparam1);
- xparam1(id_nan) = bayestopt_.p1(id_nan);
+ id_NaN = isnan(xparam1);
+ xparam1(id_NaN) = bayestopt_.p1(id_NaN);
outside_bound_pars=find(xparam1 < BoundsInfo.lb | xparam1 > BoundsInfo.ub);
xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
end
@@ -802,7 +802,7 @@ elseif strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
mom.graph_comparison_irfs(M_.matched_irfs,oo_.mom.irf_model_varobs,options_mom_.varobs_id,options_mom_.irf,options_mom_.relative_irf,M_.endo_names,M_.exo_names,M_.exo_names_tex,M_.dname,M_.fname,options_mom_.graph_format,options_mom_.TeX,options_mom_.nodisplay,options_mom_.figures.textwidth)
end
end
-% display comparison of model moments/irfs and data moments/irfs
+% display comparison of model moments/IRFs and data moments/IRFs
mom.display_comparison_moments_irfs(M_, options_mom_, oo_.mom.data_moments, oo_.mom.model_moments);
% save results to _mom_results.mat
save([M_.dname filesep 'method_of_moments' filesep M_.fname '_mom_results.mat'], 'oo_', 'options_mom_', 'M_', 'estim_params_', 'bayestopt_');
diff --git a/matlab/+mom/standard_errors.m b/matlab/+mom/standard_errors.m
index 55c64d0d28c3c2b2c22551f533202e5fcd0ba06e..c8336d68faf416b9eaeab368365a12ed8affc654 100644
--- a/matlab/+mom/standard_errors.m
+++ b/matlab/+mom/standard_errors.m
@@ -12,7 +12,7 @@ function [stderr_values, asympt_cov_mat] = standard_errors(xparam, objective_fun
% - model_moments: [vector] model moments
% - model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
% - m_data [matrix] selected empirical moments at each point in time
-% - data_moments: [vector] data with moments/irfs to match
+% - data_moments: [vector] data with moments/IRFs to match
% - weighting_info: [structure] storing information on weighting matrices
% - options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% - M_ [structure] model information