249 lines
8.3 KiB
Modula-2
249 lines
8.3 KiB
Modula-2
// --+ options: json=compute +--
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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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** 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_g_yer_u2_stn_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_g_yer_u2_stn_L1 = -0.0722359286123494 ;
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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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+ de_g_yer_u2_stn_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(-1) + res_DE_EHIC ;
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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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NSIMS = 1;
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options_.noprint = 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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nparampool = length(M_.params);
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BETA = zeros(NSIMS, nparampool);
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for i=1:NSIMS
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firstobs = rand(3, length(M_.endo_names));
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M_.params = calibrated_values;
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M_.Sigma_e = Sigma_e;
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simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_.endo_names), 10000);
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simdata = simdata(simdata.dates(5001:6000));
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names=regexp(simdata.name, 'res\w*');
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idxs = [];
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for j=1:length(names)
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if isempty(names{j})
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idxs = [idxs j];
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end
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end
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simdata = sur(simdata{idxs}, {'u2_q_yed_u2_g_yer_L1', 'u2_estn_u2_estn_L1', 'u2_ehic_u2_ehic_L1', 'de_q_yed_de_g_yer_L1', 'u2_g_yer_u2_g_yer_L1', 'u2_stn_u2_q_yed_L1', 'u2_q_yed_ecm_u2_q_yed_L1', 'de_g_yer_u2_stn_L1'}, {}, 'mymodel', true);
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BETA(i, :) = M_.params';
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oo_ = rmfield(oo_, 'sur');
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end
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if NSIMS > 1
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if max(abs(mean(BETA)' - calibrated_values)) > 1e-2
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error(['sum(abs(mean(BETA)'' - calibrated_values)) ' num2str(sum(abs(mean(BETA)' - calibrated_values)))]);
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end
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else
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if isoctave
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good = [-8.387004799504507e-01
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-3.237153385689760e-01
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1.627623494498021e-02
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5.839770395844600e-02
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1.898960469774210e-02
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-1.095976598874320e-01
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3.766796763202500e-03
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4.616983632734178e-01
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-7.223592861234940e-02
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-4.385006626083560e-02
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-1.532839171387720e-01
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5.627557877681382e-03
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2.921219497367560e-01
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1.000151654801889e+00
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1.000581567733505e+00
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-8.223751658931490e-01
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-3.237153385689770e-01
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1.795374571546572e-02
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5.839770395844610e-02
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1.898960469774220e-02
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-1.095976598874330e-01
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3.766796763202560e-03
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4.805063819236430e-01
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-5.852923523496190e-02
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1.000000000000000e+00];
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else
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good = [-0.811559435201128
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-0.323715338568976
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0.073666282610569
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0.058397703958446
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0.018989604697742
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-0.109597659887432
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0.003766796763203
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0.510389966839712
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-0.072235928612349
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-0.043850066260836
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-0.153283917138772
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0.058739144948151
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0.292121949736756
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1.000413662363949
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0.999337514406012
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-0.822375165893149
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-0.323715338568977
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0.046096335070402
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0.058397703958446
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0.018989604697742
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-0.109597659887433
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0.003766796763203
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0.480506381923643
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-0.111002148299648
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1.000000000000000];
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end
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if max(abs(BETA' - good)) > 2e-14
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error(['sum of BETA'' - good was: ' num2str(sum(abs(BETA' - good)))]);
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end
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return
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end
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for i=1:nparampool
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figure
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hold on
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title(strrep(M_.param_names(i,:), '_', '\_'));
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histogram(BETA(:,i),50);
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line([calibrated_values(i) calibrated_values(i)], [0 NSIMS/10], 'LineWidth', 2, 'Color', 'r');
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hold off
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end
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