Added integration tests (for constrained OLS).
Stéphane Adjemia (Scylla)
parent
bbf437bebd
commit
0d5b310207
GPG Key ID:
A6D44CB9C64CE77B
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@ -416,6 +416,9 @@ MODFILES = \
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pac/trend-component-17/example.mod \
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pac/trend-component-18/example.mod \
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estimation/univariate/bayesian.mod \
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estimation/univariate/ols/ols.mod \
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estimation/univariate/ols/ols_wc_1.mod \
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estimation/univariate/ols/ols_wc_2.mod \
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dynare-command-options/ramst.mod
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PARTICLEFILES = \
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@ -811,6 +814,10 @@ var-expectations: m/var-expectations o/var-expectations
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m/var-expectations: $(patsubst %.mod, %.m.trs, $(filter var-expectations/%.mod, $(MODFILES)))
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o/var-expectations: $(patsubst %.mod, %.o.trs, $(filter var-expectations/%.mod, $(MODFILES)))
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estimation/univariate/ols: m/estimation/univariate/ols o/estimation/univariate/ols
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m/estimation/univariate/ols: $(patsubst %.mod, %.m.trs, $(filter estimation/univariate/ols/%.mod, $(MODFILES)))
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o/estimation/univariate/ols: $(patsubst %.mod, %.o.trs, $(filter estimation/univariate/ols/%.mod, $(MODFILES)))
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trend-component-and-var-models: m/trend-component-and-var-models o/trend-component-and-var-models
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m/trend-component-and-var-models: $(patsubst %.mod, %.m.trs, $(filter trend-component-and-var-models/%.mod, $(MODFILES)))
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o/trend-component-and-var-models: $(patsubst %.mod, %.o.trs, $(filter trend-component-and-var-models/%.mod, $(MODFILES)))
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@ -943,6 +950,7 @@ EXTRA_DIST = \
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optimizers/fs2000.common.inc \
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estimation/MH_recover/fs2000.common.inc \
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estimation/univariate/data.csv \
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estimation/univariate/ols/mc-ols.inc \
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prior_posterior_function/posterior_function_demo.m
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@ -0,0 +1,57 @@
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verbatim;
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options_.noprint = true;
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number_of_simulations = 1000;
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calibrated_values = M_.params;
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Sigma_e = M_.Sigma_e;
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options_.bnlms.set_dynare_seed_to_default = false;
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Beta = zeros(number_of_simulations, M_.param_nbr);
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for i=1:number_of_simulations
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% Set initial conditions randomly
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firstobs = rand(3, length(M_.endo_names));
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% Set parameters to calibrated values (because after the
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% estimation parameters in equation 7 are updated with OLS
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% estimator).
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M_.params = calibrated_values;
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M_.Sigma_e = Sigma_e;
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% Simulate the model.
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simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_.endo_names), 1100);
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% Select a subsample.
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simdata = simdata(simdata.dates(101:1100));
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% Get vector of indices to exclude exogenous variables from the dataset.
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names = regexp(simdata.name, 'res\w*');
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idxs = find(cellfun(@isempty, names));
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% Perform the estimation of equation 7.
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dyn_ols(simdata{idxs}, {}, {'eq7'});
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% Store the estimation results in Beta
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Beta(i, :) = M_.params';
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end
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% Get the indices of the estimated parameters
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pid = oo_.ols.eq7.param_idxs;
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for i=1:length(pid)
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figure(i)
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hold on
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title(strrep(M_.param_names(pid(i),:), '_', '\_'));
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bandwidth = mh_optimal_bandwidth(Beta(:,pid(i)), length(Beta(:,pid(i))), -1, 'gaussian');
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[abscissa, f] = kernel_density_estimate(Beta(:,pid(i)), 256, length(Beta(:,pid(i))), bandwidth, 'gaussian');
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plot(abscissa, f, '-k', 'linewidth', 2);
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line([calibrated_values(pid(i)) calibrated_values(pid(i))], [0 max(f)*1.05], 'LineWidth', 2, 'Color', 'r');
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line([mean(Beta(:,pid(i))) mean(Beta(:,pid(i)))], [0 max(f)*1.05], 'LineWidth', 2, 'Color', 'g');
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axis tight
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box on
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hold off
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end
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if max(abs(calibrated_values(pid)-mean(Beta(:,pid)', 2)))>1e-2
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error('There is probably an error in the OLS routine.')
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end
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end;
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@ -148,3 +148,5 @@ var res_DE_Q_YED = 0.005;
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var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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@#include "mc-ols.inc"
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@ -0,0 +1,149 @@
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// --+ options: json=compute nopreprocessoroutput stochastic +--
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/* REMARK
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** ------
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**
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** You need to have the first line on top of the mod file. The options defined on this line are passed
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f** to the dynare command (you can add other options, separated by spaces or commas). The option defined
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** here is mandatory for the decomposition. It forces Dynare to output another representation of the
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** model in JSON file (additionaly to the matlab files) which is used here to manipulate the equations.
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*/
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var
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U2_Q_YED
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U2_G_YER
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U2_STN
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U2_ESTN
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U2_EHIC
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DE_Q_YED
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DE_G_YER
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DE_EHIC
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;
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varexo
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res_U2_Q_YED
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res_U2_G_YER
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res_U2_STN
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res_U2_ESTN
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res_U2_EHIC
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res_DE_Q_YED
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res_DE_G_YER
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res_DE_EHIC
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;
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parameters
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u2_q_yed_ecm_u2_q_yed_L1
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u2_q_yed_ecm_u2_stn_L1
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u2_q_yed_u2_g_yer_L1
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u2_q_yed_u2_stn_L1
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u2_g_yer_ecm_u2_q_yed_L1
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u2_g_yer_ecm_u2_stn_L1
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u2_g_yer_u2_q_yed_L1
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u2_g_yer_u2_g_yer_L1
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u2_g_yer_u2_stn_L1
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u2_stn_ecm_u2_q_yed_L1
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u2_stn_ecm_u2_stn_L1
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u2_stn_u2_q_yed_L1
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u2_stn_u2_g_yer_L1
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u2_estn_u2_estn_L1
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u2_ehic_u2_ehic_L1
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de_q_yed_ecm_de_q_yed_L1
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de_q_yed_ecm_u2_stn_L1
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de_q_yed_de_g_yer_L1
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de_q_yed_u2_stn_L1
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de_g_yer_ecm_de_q_yed_L1
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de_g_yer_ecm_u2_stn_L1
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de_g_yer_de_q_yed_L1
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de_g_yer_de_g_yer_L1
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de_ehic_de_ehic_L1
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;
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u2_q_yed_ecm_u2_q_yed_L1 = -0.82237516589315 ;
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u2_q_yed_ecm_u2_stn_L1 = -0.323715338568976 ;
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u2_q_yed_u2_g_yer_L1 = 0.0401361895021084 ;
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u2_q_yed_u2_stn_L1 = 0.058397703958446 ;
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u2_g_yer_ecm_u2_q_yed_L1 = 0.0189896046977421 ;
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u2_g_yer_ecm_u2_stn_L1 = -0.109597659887432 ;
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u2_g_yer_u2_q_yed_L1 = 0.0037667967632025 ;
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u2_g_yer_u2_g_yer_L1 = 0.480506381923644 ;
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u2_g_yer_u2_stn_L1 = -0.0722359286123494 ;
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u2_stn_ecm_u2_q_yed_L1 = -0.0438500662608356 ;
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u2_stn_ecm_u2_stn_L1 = -0.153283917138772 ;
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u2_stn_u2_q_yed_L1 = 0.0328744983772825 ;
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u2_stn_u2_g_yer_L1 = 0.292121949736756 ;
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u2_estn_u2_estn_L1 = 1 ;
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u2_ehic_u2_ehic_L1 = 1 ;
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de_q_yed_ecm_de_q_yed_L1 = -0.822375165893149 ;
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de_q_yed_ecm_u2_stn_L1 = -0.323715338568977 ;
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de_q_yed_de_g_yer_L1 = 0.0401361895021082 ;
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de_q_yed_u2_stn_L1 = 0.0583977039584461 ;
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de_g_yer_ecm_de_q_yed_L1 = 0.0189896046977422 ;
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de_g_yer_ecm_u2_stn_L1 = -0.109597659887433 ;
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de_g_yer_de_q_yed_L1 = 0.00376679676320256;
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de_g_yer_de_g_yer_L1 = 0.480506381923643 ;
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de_ehic_de_ehic_L1 = 1 ;
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model(linear);
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[name = 'eq1']
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diff(U2_Q_YED) = u2_q_yed_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
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+ u2_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
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+ u2_q_yed_u2_g_yer_L1 * diff(U2_G_YER(-1))
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+ u2_q_yed_u2_stn_L1 * diff(U2_STN(-1))
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+ res_U2_Q_YED ;
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[name = 'eq2']
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diff(U2_G_YER) = u2_g_yer_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
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+ u2_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
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+ u2_g_yer_u2_q_yed_L1 * diff(U2_Q_YED(-1))
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+ u2_g_yer_u2_g_yer_L1 * diff(U2_G_YER(-1))
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+ u2_g_yer_u2_stn_L1 * diff(U2_STN(-1))
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+ res_U2_G_YER ;
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[name = 'eq3']
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diff(U2_STN) = u2_stn_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
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+ u2_stn_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
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+ u2_stn_u2_q_yed_L1 * diff(U2_Q_YED(-1))
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+ u2_stn_u2_g_yer_L1 * diff(U2_G_YER(-1))
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+ res_U2_STN ;
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[name = 'eq4']
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U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
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+ res_U2_ESTN ;
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[name = 'eq5']
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U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
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+ res_U2_EHIC ;
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[name = 'eq6']
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diff(DE_Q_YED) = de_q_yed_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
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+ de_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
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+ de_q_yed_de_g_yer_L1 * diff(DE_G_YER(-1))
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+ de_q_yed_u2_stn_L1 * diff(U2_STN(-1))
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+ res_DE_Q_YED ;
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[name = 'eq7']
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diff(DE_G_YER) = de_g_yer_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
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+ de_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
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+ de_g_yer_de_q_yed_L1 * diff(DE_Q_YED(-1))
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+ de_g_yer_de_g_yer_L1 * diff(DE_G_YER(-1))
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+ (1-de_g_yer_de_q_yed_L1-de_g_yer_de_g_yer_L1)* diff(U2_STN(-1))
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+ res_DE_G_YER ;
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[name = 'eq8']
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DE_EHIC = de_ehic_de_ehic_L1 * DE_EHIC(-1)
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+ res_DE_EHIC ;
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//
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end;
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shocks;
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var res_U2_Q_YED = 0.005;
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var res_U2_G_YER = 0.005;
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var res_U2_STN = 0.005;
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var res_U2_ESTN = 0.005;
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var res_U2_EHIC = 0.005;
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var res_DE_Q_YED = 0.005;
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var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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@#include "mc-ols.inc"
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@ -0,0 +1,152 @@
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// --+ options: json=compute nopreprocessoroutput stochastic +--
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/* REMARK
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** ------
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**
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** You need to have the first line on top of the mod file. The options defined on this line are passed
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f** to the dynare command (you can add other options, separated by spaces or commas). The option defined
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** here is mandatory for the decomposition. It forces Dynare to output another representation of the
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** model in JSON file (additionaly to the matlab files) which is used here to manipulate the equations.
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*/
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var
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U2_Q_YED
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U2_G_YER
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U2_STN
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U2_ESTN
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U2_EHIC
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DE_Q_YED
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DE_G_YER
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DE_EHIC
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;
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varexo
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res_U2_Q_YED
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res_U2_G_YER
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res_U2_STN
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res_U2_ESTN
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res_U2_EHIC
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res_DE_Q_YED
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res_DE_G_YER
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res_DE_EHIC
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;
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parameters
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u2_q_yed_ecm_u2_q_yed_L1
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u2_q_yed_ecm_u2_stn_L1
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u2_q_yed_u2_g_yer_L1
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u2_q_yed_u2_stn_L1
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u2_g_yer_ecm_u2_q_yed_L1
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u2_g_yer_ecm_u2_stn_L1
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u2_g_yer_u2_q_yed_L1
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u2_g_yer_u2_g_yer_L1
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u2_g_yer_u2_stn_L1
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u2_stn_ecm_u2_q_yed_L1
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u2_stn_ecm_u2_stn_L1
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u2_stn_u2_q_yed_L1
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u2_stn_u2_g_yer_L1
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u2_estn_u2_estn_L1
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u2_ehic_u2_ehic_L1
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de_q_yed_ecm_de_q_yed_L1
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de_q_yed_ecm_u2_stn_L1
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de_q_yed_de_g_yer_L1
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de_q_yed_u2_stn_L1
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de_g_yer_ecm_de_q_yed_L1
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de_g_yer_ecm_u2_stn_L1
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de_g_yer_de_q_yed_L1
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de_g_yer_de_g_yer_L1
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de_ehic_de_ehic_L1
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p
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;
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u2_q_yed_ecm_u2_q_yed_L1 = -0.82237516589315 ;
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u2_q_yed_ecm_u2_stn_L1 = -0.323715338568976 ;
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u2_q_yed_u2_g_yer_L1 = 0.0401361895021084 ;
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u2_q_yed_u2_stn_L1 = 0.058397703958446 ;
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u2_g_yer_ecm_u2_q_yed_L1 = 0.0189896046977421 ;
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u2_g_yer_ecm_u2_stn_L1 = -0.109597659887432 ;
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u2_g_yer_u2_q_yed_L1 = 0.0037667967632025 ;
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u2_g_yer_u2_g_yer_L1 = 0.480506381923644 ;
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u2_g_yer_u2_stn_L1 = -0.0722359286123494 ;
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u2_stn_ecm_u2_q_yed_L1 = -0.0438500662608356 ;
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u2_stn_ecm_u2_stn_L1 = -0.153283917138772 ;
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u2_stn_u2_q_yed_L1 = 0.0328744983772825 ;
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u2_stn_u2_g_yer_L1 = 0.292121949736756 ;
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u2_estn_u2_estn_L1 = 1 ;
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u2_ehic_u2_ehic_L1 = 1 ;
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de_q_yed_ecm_de_q_yed_L1 = -0.822375165893149 ;
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de_q_yed_ecm_u2_stn_L1 = -0.323715338568977 ;
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de_q_yed_de_g_yer_L1 = 0.0401361895021082 ;
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de_q_yed_u2_stn_L1 = 0.0583977039584461 ;
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de_g_yer_ecm_de_q_yed_L1 = 0.0189896046977422 ;
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de_g_yer_ecm_u2_stn_L1 = -0.109597659887433 ;
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de_g_yer_de_q_yed_L1 = 0.00376679676320256;
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de_g_yer_de_g_yer_L1 = 0.480506381923643 ;
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de_ehic_de_ehic_L1 = 1 ;
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p = 0;
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model(linear);
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[name = 'eq1']
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diff(U2_Q_YED) = u2_q_yed_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
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+ u2_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
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+ u2_q_yed_u2_g_yer_L1 * diff(U2_G_YER(-1))
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+ u2_q_yed_u2_stn_L1 * diff(U2_STN(-1))
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+ res_U2_Q_YED ;
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[name = 'eq2']
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||||
diff(U2_G_YER) = u2_g_yer_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
|
||||
+ u2_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ u2_g_yer_u2_q_yed_L1 * diff(U2_Q_YED(-1))
|
||||
+ u2_g_yer_u2_g_yer_L1 * diff(U2_G_YER(-1))
|
||||
+ u2_g_yer_u2_stn_L1 * diff(U2_STN(-1))
|
||||
+ res_U2_G_YER ;
|
||||
[name = 'eq3']
|
||||
diff(U2_STN) = u2_stn_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
|
||||
+ u2_stn_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ u2_stn_u2_q_yed_L1 * diff(U2_Q_YED(-1))
|
||||
+ u2_stn_u2_g_yer_L1 * diff(U2_G_YER(-1))
|
||||
+ res_U2_STN ;
|
||||
[name = 'eq4']
|
||||
U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
|
||||
+ res_U2_ESTN ;
|
||||
[name = 'eq5']
|
||||
U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
|
||||
+ res_U2_EHIC ;
|
||||
[name = 'eq6']
|
||||
diff(DE_Q_YED) = de_q_yed_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
|
||||
+ de_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ de_q_yed_de_g_yer_L1 * diff(DE_G_YER(-1))
|
||||
+ de_q_yed_u2_stn_L1 * diff(U2_STN(-1))
|
||||
+ res_DE_Q_YED ;
|
||||
[name = 'eq7']
|
||||
diff(DE_G_YER) = de_g_yer_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
|
||||
+ de_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ de_g_yer_de_q_yed_L1 * diff(DE_Q_YED(-1))
|
||||
+ de_g_yer_de_g_yer_L1 * diff(DE_G_YER(-1))
|
||||
+ (p-de_g_yer_de_q_yed_L1) * diff(U2_STN(-1))
|
||||
+ res_DE_G_YER ;
|
||||
[name = 'eq8']
|
||||
DE_EHIC = de_ehic_de_ehic_L1 * DE_EHIC(-1)
|
||||
+ res_DE_EHIC ;
|
||||
|
||||
//
|
||||
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var res_U2_Q_YED = 0.005;
|
||||
var res_U2_G_YER = 0.005;
|
||||
var res_U2_STN = 0.005;
|
||||
var res_U2_ESTN = 0.005;
|
||||
var res_U2_EHIC = 0.005;
|
||||
var res_DE_Q_YED = 0.005;
|
||||
var res_DE_G_YER = 0.005;
|
||||
var res_DE_EHIC = 0.005;
|
||||
end;
|
||||
|
||||
@#include "mc-ols.inc"
|
||||
|
|
@ -1,52 +0,0 @@
|
|||
close all
|
||||
|
||||
dynare ols.mod;
|
||||
|
||||
global M_ options_ oo_
|
||||
|
||||
|
||||
options_.noprint = true;
|
||||
|
||||
number_of_simulations = 1000;
|
||||
|
||||
calibrated_values = M_.params;
|
||||
Sigma_e = M_.Sigma_e;
|
||||
|
||||
options_.bnlms.set_dynare_seed_to_default = false;
|
||||
|
||||
Beta = zeros(number_of_simulations, M_.param_nbr);
|
||||
|
||||
for i=1:number_of_simulations
|
||||
% Set initial conditions randomly
|
||||
firstobs = rand(3, length(M_.endo_names));
|
||||
% Set parameters to calibrated values (because after the
|
||||
% estimation parameters in equation 7 are updated with OLS
|
||||
% estimator).
|
||||
M_.params = calibrated_values;
|
||||
M_.Sigma_e = Sigma_e;
|
||||
% Simulate the model.
|
||||
simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_.endo_names), 10000);
|
||||
% Select a subsample.
|
||||
simdata = simdata(simdata.dates(5001:6000));
|
||||
% Perform the estimation of equation 7.
|
||||
names = regexp(simdata.name, 'res\w*');
|
||||
idxs = find(cellfun(@isempty, names));
|
||||
dyn_ols(simdata{idxs}, {}, {'eq7'});
|
||||
% Store the estimation results in Beta
|
||||
Beta(i, :) = M_.params';
|
||||
end
|
||||
|
||||
pid = oo_.ols.eq7.param_idxs;
|
||||
|
||||
for i=1:length(pid)
|
||||
figure(i)
|
||||
hold on
|
||||
title(strrep(M_.param_names(pid(i),:), '_', '\_'));
|
||||
bandwidth = mh_optimal_bandwidth(Beta(:,pid(i)), length(Beta(:,pid(i))), -1, 'gaussian');
|
||||
[abscissa, f] = kernel_density_estimate(Beta(:,pid(i)), 256, length(Beta(:,pid(i))), bandwidth, 'gaussian');
|
||||
plot(abscissa, f, '-k', 'linewidth', 2);
|
||||
line([calibrated_values(pid(i)) calibrated_values(pid(i))], [0 max(f)*1.05], 'LineWidth', 2, 'Color', 'r');
|
||||
axis tight
|
||||
box on
|
||||
hold off
|
||||
end
|
||||
Loading…
Reference in New Issue