105 lines
3.7 KiB
PHP
105 lines
3.7 KiB
PHP
verbatim;
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options_.noprint = true;
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number_of_simulations = 1;
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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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if strcmp(M_.fname, 'ols_param_names')
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dyn_ols(simdata{idxs}, {}, {'eq7'}, '', {{'de_g_yer_ecm_u2_stn_L1', 'de_g_yer_de_g_yer_L1'}});
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elseif strcmp(M_.fname, 'ols_date_range')
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dyn_ols(simdata{idxs}, {}, {'eq7'}, '', {}, simdata.dates(2:end-3));
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else
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dyn_ols(simdata{idxs}, {}, {'eq7'});
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end
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if i ~= number_of_simulations
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oo_ = rmfield(oo_, 'ols');
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end
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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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if number_of_simulations > 1
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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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else
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if strcmp(M_.fname, 'ols')
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good = [0.025487074270635
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-0.126705792250240
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0.002639663639062
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0.486913131141265
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-0.027702100375691];
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elseif strcmp(M_.fname, 'ols_param_names')
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good = [-0.125265361034515
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0.497003481797959];
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elseif strcmp(M_.fname, 'ols_date_range')
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good = [0.468607715487806
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-0.011224442004140
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0.033681224841824
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-0.126743281213504];
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elseif strcmp(M_.fname, 'ols_wc_1')
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good = [0.466534186307255
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-0.010596174928406
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0.035098464382347
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-0.126502172176703];
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elseif strcmp(M_.fname, 'ols_wc_2')
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good = [0.485802603276717
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0.002660854661647
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0.025434782170432
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-0.126939890810119
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0.043688440124315];
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elseif strcmp(M_.fname, 'ols_wc_3')
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good = [0.490636551206316
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0.014209413280418
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0.028534044956486
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-0.094387460262917];
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end
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if max(abs(good-Beta(:,pid)'))>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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return
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end
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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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end;
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