update tests
Houtan Bastani
parent
01c5d2a90c
commit
59ab4f4b34
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@ -437,16 +437,17 @@ MODFILES = \
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dynare-command-options/ramst.mod
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dynare-command-options/ramst.mod
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OLS_MODFILES = \
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OLS_MODFILES = \
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estimation/univariate/bayesian.mod \
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ecb/SURGibbs/fulton_fish.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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estimation/univariate/ols/ols.mod \
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ecb/SUR/sur_noninteractive.mod \
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ecb/SUR/sur_noninteractive.mod \
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ecb/SUR/panel_var_diff_NB_simulation_test.mod \
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ecb/SUR/panel_var_diff_NB_simulation_test.mod \
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ecb/SUR/panel_var_diff_NB_simulation_zero_eq.mod \
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ecb/SUR/panel_var_diff_NB_simulation_zero_eq.mod \
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ecb/pooled_ols/panel_var_diff_NB_simulation_test.mod \
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ecb/pooled_ols/panel_var_diff_NB_simulation_test.mod \
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ecb/pooled_fgls/panel_var_diff_NB_simulation_test.mod \
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ecb/pooled_fgls/panel_var_diff_NB_simulation_test.mod
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estimation/univariate/bayesian.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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estimation/univariate/ols/ols.mod \
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ecb/SURGibbs/fulton_fish.mod
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MODFILES += $(OLS_MODFILES)
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MODFILES += $(OLS_MODFILES)
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@ -148,14 +148,13 @@ var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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end;
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NSIMS = 1000;
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NSIMS = 1;
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options_.noprint = 1;
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options_.noprint = 1;
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calibrated_values = M_.params;
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calibrated_values = M_.params;
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verbatim;
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verbatim;
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Sigma_e = M_.Sigma_e;
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Sigma_e = M_.Sigma_e;
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end;
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end;
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assert(NSIMS > 1);
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options_.bnlms.set_dynare_seed_to_default = false;
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options_.bnlms.set_dynare_seed_to_default = false;
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nparampool = length(M_.params);
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nparampool = length(M_.params);
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@ -175,36 +174,43 @@ for i=1:NSIMS
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end
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end
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sur(simdata{idxs});
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sur(simdata{idxs});
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BETA(i, :) = M_.params';
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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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end
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tmp = mean(BETA)' - calibrated_values;
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if NSIMS > 1
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good = [-0.000851206009290
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if max(abs(mean(BETA)' - calibrated_values)) > 1e-2
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-0.000643198850342
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error(['sum(abs(mean(BETA)'' - calibrated_values)) ' num2str(sum(abs(mean(BETA)' - calibrated_values)))]);
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-0.000077654636303
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end
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-0.000420898437454
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else
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0.000262557551524
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good = [-0.826686196809409
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-0.000245111192739
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-0.346753563700393
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-0.000076643809917
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0.063013991583949
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-0.001731735812364
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0.074802596658698
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-0.001143888514328
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-0.017440119721953
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0.000121014793695
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-0.127090614348862
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-0.000577022127737
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0.025293280404460
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-0.000749730989778
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0.524290302468866
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-0.000277999433209
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-0.117611206771440
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-0.000622189775653
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-0.027776224547132
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-0.000635550804554
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-0.156590828735908
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-0.000249226746560
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0.054039707976331
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-0.000444130561721
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0.276257666502046
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-0.000186765256265
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1.000417289621684
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-0.000441923342534
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0.999336865129450
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-0.000014925384893
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-0.803258152338916
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0.000091140073668
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-0.309594948488168
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-0.000237339805772
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0.051602756230521
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-0.000838249050874
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0.039275481081030
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0.000815931859253
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0.024897596371662
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0];
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-0.096310133845385
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if sum(abs(tmp-good)) > 1e-14
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-0.022630284059365
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error(['sum of tmp - good was: ' num2str(sum(abs(tmp-good)))]);
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0.461683465196454
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-0.110278113383114
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1.000000000000000];
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if max(abs(BETA' - good)) > 1e-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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end
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for i=1:nparampool
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for i=1:nparampool
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@ -144,7 +144,7 @@ var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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end;
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NSIMS = 1000;
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NSIMS = 1;
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options_.noprint = 1;
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options_.noprint = 1;
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calibrated_values = M_.params;
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calibrated_values = M_.params;
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@ -173,42 +173,43 @@ for i=1:NSIMS
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end
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end
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sur(simdata{idxs});
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sur(simdata{idxs});
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BETA(i, :) = M_.params';
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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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end
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if size(BETA',2) == 1
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if NSIMS > 1
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tmp = BETA' - calibrated_values;
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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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else
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tmp = mean(BETA)' - calibrated_values;
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good = [-0.806260229719792
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end
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-0.354371215026078
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0.067618717060638
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good = [-0.003887531498207
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0.088053231576354
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-0.002565330545918
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-0.057161898696690
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-0.000239567336676
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-0.136999143481575
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0.003408443512070
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0.052047088887385
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0.001553818170767
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0.524022502978068
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-0.000469903487876
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-0.110211572532150
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-0.001112844922537
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-0.041415758049800
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-0.003118147971245
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-0.162997606880957
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0.000781155700883
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0.091000830792721
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-0.000803694625262
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0.281196660545012
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-0.002236638387246
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1.000000000000000
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-0.000683200861198
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1.000000000000000
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-0.000310394894060
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-0.823354793011220
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0
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-0.337521089381118
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0
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0.025493177305917
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-0.000086918837962
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0.059513905441361
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-0.000383472263486
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0.016845886773011
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0.000820117983637
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-0.151537340795868
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0.000953382489529
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0.004832174649536
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0.000989541137148
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0.430883913061161
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-0.001083720105374
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-0.071743697511067
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-0.000359301045695
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1.000000000000000];
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-0.001656187747193
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if max(abs(BETA' - good)) > 1e-14
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0.000462206249631
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error(['sum of BETA'' - good was: ' num2str(sum(abs(BETA - good)))]);
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0];
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end
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return
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if sum(abs(tmp-good)) > 1e-14
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error(['sum of tmp - good was: ' num2str(sum(abs(tmp-good)))]);
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end
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end
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for i=1:nparampool
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for i=1:nparampool
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@ -148,14 +148,13 @@ var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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end;
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NSIMS = 1000;
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NSIMS = 1;
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options_.noprint = 1;
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options_.noprint = 1;
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calibrated_values = M_.params;
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calibrated_values = M_.params;
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verbatim;
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verbatim;
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Sigma_e = M_.Sigma_e;
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Sigma_e = M_.Sigma_e;
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end;
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end;
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assert(NSIMS > 1);
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options_.bnlms.set_dynare_seed_to_default = false;
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options_.bnlms.set_dynare_seed_to_default = false;
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nparampool = length(M_.params);
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nparampool = length(M_.params);
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@ -175,36 +174,43 @@ for i=1:NSIMS
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end
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end
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sur(simdata{idxs}, {}, {}, 'mymodel', true);
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sur(simdata{idxs}, {}, {}, 'mymodel', true);
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BETA(i, :) = M_.params';
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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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end
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tmp = mean(BETA)' - calibrated_values;
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if NSIMS > 1
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good = [-0.000851256219733
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if max(abs(mean(BETA)' - calibrated_values)) > 1e-2
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-0.000643255670073
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error(['sum(abs(mean(BETA)'' - calibrated_values)) ' num2str(sum(abs(mean(BETA)' - calibrated_values)))]);
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-0.000078774226193
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end
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-0.000420026410496
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else
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0.000262319820667
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good = [-0.826686383251952
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-0.000245434738588
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-0.346754226737713
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-0.000076193984629
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0.063012625739192
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-0.001732502219600
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0.074803008678581
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-0.001142802053612
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-0.017406695286611
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0.000120684867379
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-0.127075466955954
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-0.000577323101449
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0.025273119798253
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-0.000750502207473
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0.524311285013928
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-0.000277709424315
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-0.117653504652072
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-0.000622119904600
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-0.027795769946178
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-0.000635564841533
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-0.156599054434094
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-0.000249342272071
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0.054048691636616
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-0.000443999454424
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0.276250884768198
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-0.000187590389264
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1.000417819589230
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-0.000441672489341
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0.999336022919911
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-0.000015259855753
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-0.803262845969268
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0.000091236076295
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-0.309598590696623
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-0.000236028177813
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0.051598925732525
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-0.000838101091676
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0.039275097813797
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0.000815800740293
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0.024902442384228
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0];
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-0.096310332265968
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if sum(abs(tmp-good)) > 1e-14
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-0.022625946626029
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error(['sum of tmp - good was: ' num2str(sum(abs(tmp-good)))]);
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0.461672601960238
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-0.110232456667380
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1.000000000000000];
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if max(abs(BETA' - good)) > 1e-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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end
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for i=1:nparampool
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for i=1:nparampool
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@ -149,7 +149,7 @@ var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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end;
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NSIMS = 1000;
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NSIMS = 1;
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calibrated_values = M_.params;
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calibrated_values = M_.params;
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verbatim;
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verbatim;
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@ -186,36 +186,43 @@ for i=1:NSIMS
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'*_g_yer_u2_stn_L1', ...
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'*_g_yer_u2_stn_L1', ...
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'*_ehic_*_ehic_L1'});
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'*_ehic_*_ehic_L1'});
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BETA(i, :) = M_.params';
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BETA(i, :) = M_.params';
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oo_ = rmfield(oo_, 'pooled_fgls');
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end
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end
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tmp = mean(BETA)' - calibrated_values;
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if NSIMS > 1
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good = [-0.117180965691355
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if sum(abs(mean(BETA)' - calibrated_values)) > 1e-2
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-0.416020161883635
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error(['sum(abs(mean(BETA)'' - calibrated_values)) ' num2str(sum(abs(mean(BETA)' - calibrated_values)))]);
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-0.163402818556098
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end
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-0.590340328553228
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else
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0.146701546733206
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good = [-0.814463611096955
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0.095280068863474
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-0.327545854281441
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-0.110518319499523
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0.058155751549620
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-0.281212762467509
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0.056956172127541
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-0.387430919215850
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0.004760467203543
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0.148014899155655
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-0.110209140210870
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-0.582272479386248
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0.000873625473015
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-0.768003006461973
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0.492984603730353
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-0.280750812328145
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-0.110739844810764
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-0.622414692972750
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-0.026953553332579
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-0.213383844465564
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-0.155433250333810
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-0.117180965692354
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0.053391289583053
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-0.416020161882635
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0.275361374992940
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-0.163402818555897
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1.000446511792020
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-0.590340328553332
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0.999278815424852
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0.146701546733106
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-0.814463611096955
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0.095280068864473
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-0.327545854281441
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-0.110518319499583
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0.058155751549620
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-0.281212762466510
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0.056956172127541
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-0.387430919215850
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0.004760467203543
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-0.213383844465564]*1.0e-03;
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-0.110209140210870
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if sum(abs(tmp-good)) > 1e-14
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0.000873625473015
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error(['sum of tmp - good was: ' num2str(sum(abs(tmp-good)))]);
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0.492984603730353
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-0.110739844810764
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0.999278815424852];
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if max(abs(BETA' - good)) > 1e-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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end
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for i=1:nparampool
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for i=1:nparampool
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@ -149,7 +149,7 @@ var res_DE_G_YER = 0.005;
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var res_DE_EHIC = 0.005;
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var res_DE_EHIC = 0.005;
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end;
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end;
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NSIMS = 1000;
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NSIMS = 1;
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calibrated_values = M_.params;
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calibrated_values = M_.params;
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verbatim;
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verbatim;
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@ -186,36 +186,43 @@ for i=1:NSIMS
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'*_g_yer_u2_stn_L1', ...
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'*_g_yer_u2_stn_L1', ...
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'*_ehic_*_ehic_L1'});
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'*_ehic_*_ehic_L1'});
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BETA(i, :) = M_.params';
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BETA(i, :) = M_.params';
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oo_ = rmfield(oo_, 'pooled_ols');
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end
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end
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tmp = mean(BETA)' - calibrated_values;
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if NSIMS > 1
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good = [-0.075732742267465
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if sum(abs(mean(BETA)' - calibrated_values)) > 1e-2
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-0.399406249294632
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error(['sum(abs(mean(BETA)'' - calibrated_values)) ' num2str(sum(abs(mean(BETA)' - calibrated_values)))]);
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-0.234792967895359
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end
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-0.588970916491835
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else
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0.067498492395235
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good = [-0.814685080218300
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0.063450881343338
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-0.327809964140588
|
||||||
-0.039680797111538
|
0.058359966460475
|
||||||
-0.305307448765335
|
0.056151094599907
|
||||||
-0.347337705286735
|
0.004707755717058
|
||||||
0.094723439006586
|
-0.110288651670160
|
||||||
-0.576221284678741
|
0.000910770641800
|
||||||
-0.789746631585334
|
0.494342669441397
|
||||||
-0.264217514455656
|
-0.109455922483561
|
||||||
-0.620788021980068
|
-0.029329632163194
|
||||||
-0.213308690451885
|
-0.157236653586389
|
||||||
-0.075732742268464
|
0.054467548349538
|
||||||
-0.399406249293632
|
0.275969658826900
|
||||||
-0.234792967895157
|
1.000451016087141
|
||||||
-0.588970916491939
|
0.999236021952814
|
||||||
0.067498492395134
|
-0.814685080218300
|
||||||
0.063450881344337
|
-0.327809964140588
|
||||||
-0.039680797111598
|
0.058359966460475
|
||||||
-0.305307448764336
|
0.056151094599907
|
||||||
-0.347337705286735
|
0.004707755717058
|
||||||
-0.213308690451885]*1.0e-03;
|
-0.110288651670160
|
||||||
if sum(abs(tmp-good)) > 1e-14
|
0.000910770641800
|
||||||
error(['sum of tmp - good was: ' num2str(sum(abs(tmp-good)))]);
|
0.494342669441397
|
||||||
|
-0.109455922483561
|
||||||
|
0.999236021952814];
|
||||||
|
if max(abs(BETA' - good)) > 1e-14
|
||||||
|
error(['sum of BETA'' - good was: ' num2str(sum(abs(BETA - good)))]);
|
||||||
|
end
|
||||||
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
for i=1:nparampool
|
for i=1:nparampool
|
||||||
|
|
|
||||||
|
|
@ -2,7 +2,7 @@ verbatim;
|
||||||
|
|
||||||
options_.noprint = true;
|
options_.noprint = true;
|
||||||
|
|
||||||
number_of_simulations = 1000;
|
number_of_simulations = 1;
|
||||||
|
|
||||||
calibrated_values = M_.params;
|
calibrated_values = M_.params;
|
||||||
Sigma_e = M_.Sigma_e;
|
Sigma_e = M_.Sigma_e;
|
||||||
|
|
@ -28,6 +28,9 @@ verbatim;
|
||||||
idxs = find(cellfun(@isempty, names));
|
idxs = find(cellfun(@isempty, names));
|
||||||
% Perform the estimation of equation 7.
|
% Perform the estimation of equation 7.
|
||||||
dyn_ols(simdata{idxs}, {}, {'eq7'});
|
dyn_ols(simdata{idxs}, {}, {'eq7'});
|
||||||
|
if i ~= number_of_simulations
|
||||||
|
oo_ = rmfield(oo_, 'ols');
|
||||||
|
end
|
||||||
% Store the estimation results in Beta
|
% Store the estimation results in Beta
|
||||||
Beta(i, :) = M_.params';
|
Beta(i, :) = M_.params';
|
||||||
end
|
end
|
||||||
|
|
@ -35,6 +38,35 @@ verbatim;
|
||||||
% Get the indices of the estimated parameters
|
% Get the indices of the estimated parameters
|
||||||
pid = oo_.ols.eq7.param_idxs;
|
pid = oo_.ols.eq7.param_idxs;
|
||||||
|
|
||||||
|
if number_of_simulations > 1
|
||||||
|
if max(abs(calibrated_values(pid)-mean(Beta(:,pid)', 2)))>1e-2
|
||||||
|
error('There is probably an error in the OLS routine.')
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if strcmp(M_.fname, 'ols')
|
||||||
|
good = [-0.027702100375691
|
||||||
|
0.486913131141265
|
||||||
|
0.002639663639062
|
||||||
|
0.025487074270635
|
||||||
|
-0.126705792250240];
|
||||||
|
elseif strcmp(M_.fname, 'ols_wc_1')
|
||||||
|
good = [0.466534186307255
|
||||||
|
-0.010596174928406
|
||||||
|
0.035098464382347
|
||||||
|
-0.126502172176703];
|
||||||
|
elseif strcmp(M_.fname, 'ols_wc_2')
|
||||||
|
good = [0.485802603276717
|
||||||
|
0.002660854661647
|
||||||
|
-0.043688440124315
|
||||||
|
0.025434782170432
|
||||||
|
-0.126939890810119];
|
||||||
|
end
|
||||||
|
if max(abs(good-Beta(:,pid)'))>1e-2
|
||||||
|
error('There is probably an error in the OLS routine.')
|
||||||
|
end
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
for i=1:length(pid)
|
for i=1:length(pid)
|
||||||
figure(i)
|
figure(i)
|
||||||
hold on
|
hold on
|
||||||
|
|
@ -49,9 +81,5 @@ verbatim;
|
||||||
hold off
|
hold off
|
||||||
end
|
end
|
||||||
|
|
||||||
if max(abs(calibrated_values(pid)-mean(Beta(:,pid)', 2)))>1e-2
|
|
||||||
error('There is probably an error in the OLS routine.')
|
|
||||||
end
|
|
||||||
|
|
||||||
end;
|
end;
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue