Starting preprocessing of the model file ... Substitution of endo lags >= 2: added 2 auxiliary variables and equations. Found 53 equation(s). Evaluating expressions...done Computing static model derivatives (order 1). Computing dynamic model derivatives (order 2). Processing outputs ... done Preprocessing completed. You did not declare endogenous variables after the estimation/calib_smoother command. Smoothed variables will be computed for the 51 endogenous variables of your model, this can take a long time .... Choose one of the following options: [1] Consider all the endogenous variables. [2] Consider all the observed endogenous variables. [3] Stop Dynare and change the mod file. options [default is 1] = 1 Initial value of the log posterior (or likelihood): -2874.7786 ========================================================== Change in the posterior covariance matrix = 10. Change in the posterior mean = 0.55739. Mode improvement = 1882.5151 New value of jscale = 0.00038099 ========================================================== ========================================================== Change in the posterior covariance matrix = 0.068778. Change in the posterior mean = 2.8057. Mode improvement = 656.3488 New value of jscale = 0.41249 ========================================================== ========================================================== Change in the posterior covariance matrix = 0.062431. Change in the posterior mean = 1.7122. Mode improvement = 480.4617 New value of jscale = 0.34248 ========================================================== Optimal value of the scale parameter = 0.34248 Final value of minus the log posterior (or likelihood):1168.150585 MODE CHECK Fval obtained by the minimization routine (minus the posterior/likelihood)): 1168.150585 [警告: 行列が特異なため、正確に処理できません。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列は、特異行列に近いか、正しくスケーリングされていません。結果は不正確になる可能性があります。RCOND = 5.239063e-21。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列は、特異行列に近いか、正しくスケーリングされていません。結果は不正確になる可能性があります。RCOND = 7.727338e-22。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列は、特異行列に近いか、正しくスケーリングされていません。結果は不正確になる可能性があります。RCOND = 4.696307e-22。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列が特異なため、正確に処理できません。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列が特異なため、正確に処理できません。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列が特異なため、正確に処理できません。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] [警告: 行列が特異なため、正確に処理できません。] [> evaluate_steady_state (行 261) 内 resol (行 61) 内 compute_decision_rules (行 35) 内 dynare_resolve (行 69) 内 dsge_var_likelihood (行 156) 内 mode_check (行 161) 内 dynare_estimation_1 (行 322) 内 dynare_estimation (行 105) 内 jpmodel5.driver (行 842) 内 dynare (行 293) 内 ] RESULTS FROM POSTERIOR ESTIMATION parameters prior mean mode s.d. prior pstdev dsge_prior_weight 1.000 0.9867 0.0083 unif 0.5774 v_gm 0.000 1.1063 0.0322 norm 1.5000 v_gp 0.000 0.4141 0.0238 norm 1.5000 v 0.250 0.1678 0.0159 norm 1.0000 omega 0.250 0.1737 0.0042 beta 0.1000 sigma 1.000 1.2520 0.0363 gamm 0.3750 theta 0.700 0.4970 0.0089 beta 0.1500 chi 2.000 2.6843 0.0446 gamm 0.7500 zetainv 4.000 4.3963 0.0452 gamm 1.5000 mu 1.000 1.0480 0.0908 gamm 1.0000 phi 0.075 0.0779 0.0005 gamm 0.0125 gammaw 0.500 0.4326 0.0104 beta 0.2500 xiw 0.375 0.5426 0.0042 beta 0.1000 gammap 0.500 0.9705 0.0155 beta 0.2500 xip 0.375 0.5602 0.0046 beta 0.1000 lambdap 0.150 0.2427 0.0025 gamm 0.0500 zstar 0.190 0.1909 0.0048 gamm 0.0500 lstar 0.000 -0.0245 0.0023 norm 0.0500 pistar 0.175 0.1267 0.0010 gamm 0.0500 rstar 0.498 0.5201 0.0021 gamm 0.0500 phi_r 0.800 0.7449 0.0031 beta 0.1000 phi_r_pi 0.500 0.4592 0.0126 norm 0.2000 phi_r_y 0.125 0.0823 0.0008 gamm 0.0500 phi_gm 0.125 0.7445 0.0017 beta 0.0500 phi_gm_y 0.000 1.4087 0.0210 norm 0.5000 phi_gm_b 0.000 1.0212 0.0259 norm 0.5000 phi_gp 0.800 0.8391 0.0033 beta 0.1000 phi_gp_y 0.000 0.2792 0.0168 norm 0.5000 phi_gp_b 0.000 -0.0598 0.0088 norm 0.5000 phi_gi 0.800 0.7668 0.0024 beta 0.1000 phi_gi_y 0.000 0.2803 0.0100 norm 0.5000 phi_gi_b 0.000 -0.1518 0.0149 norm 0.5000 phi_T 0.800 0.9178 0.0026 beta 0.1000 phi_T_y 0.000 -0.2708 0.0127 norm 0.5000 phi_T_b 0.000 -0.0680 0.0187 norm 0.5000 phi_tc 0.800 0.8605 0.0023 beta 0.1000 phi_tc_y 0.000 -0.0034 0.0380 norm 0.5000 phi_tc_b 0.000 -0.0025 0.0247 norm 0.5000 phi_tw 0.800 0.8002 0.0019 beta 0.1000 phi_tw_y 0.000 -0.1736 0.0218 norm 0.5000 phi_tw_b 0.000 0.0597 0.0053 norm 0.5000 phi_tk 0.800 0.8212 0.0028 beta 0.1000 phi_tk_y 0.000 0.0345 0.0099 norm 0.5000 phi_tk_b 0.000 0.0383 0.0136 norm 0.5000 rho_z 0.500 0.2234 0.0061 beta 0.2000 rho_b 0.500 0.2939 0.0096 beta 0.2000 rho_i 0.500 0.4579 0.0039 beta 0.2000 rho_w 0.500 0.6570 0.0040 beta 0.2000 rho_p 0.500 0.5564 0.0020 beta 0.2000 rho_x 0.500 0.8254 0.0056 beta 0.2000 rho_r 0.500 0.4978 0.0064 beta 0.2000 rho_gm 0.500 0.5579 0.0090 beta 0.2000 rho_gp 0.500 0.0576 0.0060 beta 0.2000 rho_gi 0.500 0.4397 0.0029 beta 0.2000 rho_T 0.500 0.1034 0.0069 beta 0.2000 standard deviation of shocks prior mean mode s.d. prior pstdev e_z 0.100 0.5437 0.0669 invg Inf e_b 0.100 1.4655 0.0973 invg Inf e_i 0.100 1.9719 0.1785 invg Inf e_w 0.100 0.5048 0.0244 invg Inf e_p 0.100 0.3061 0.0418 invg Inf e_x 0.100 4.0029 0.2757 invg Inf e_r 0.100 0.2062 0.0192 invg Inf e_gm 0.100 0.4992 0.0310 invg Inf e_gp 0.100 0.7118 0.0419 invg Inf e_gi 0.100 1.6939 0.0547 invg Inf e_T 0.100 0.0475 0.0363 invg Inf e_tc 0.100 0.0078 0.0007 invg Inf e_tw 0.100 0.4474 0.0011 invg Inf e_tk 0.100 0.0091 0.0008 invg Inf Log data density [Laplace approximation] is -1601.676070. Estimation::mcmc: Multiple chains mode. Estimation::mcmc: Old metropolis.log file successfully erased! Estimation::mcmc: Creation of a new metropolis.log file. Estimation::mcmc: Searching for initial values... Estimation::mcmc: Initial values found! Estimation::mcmc: Write details about the MCMC... Ok! Estimation::mcmc: Details about the MCMC are available in jpmodel5/metropolis/jpmodel5_mh_history_0.mat Estimation::mcmc: Number of mh files: 2 per block. Estimation::mcmc: Total number of generated files: 4. Estimation::mcmc: Total number of iterations: 200000. Estimation::mcmc: Current acceptance ratio per chain: Chain 1: 41.0335% Chain 2: 37.1695% Estimation::mcmc: Total number of MH draws per chain: 200000. Estimation::mcmc: Total number of generated MH files: 2. Estimation::mcmc: I'll use mh-files 1 to 2. Estimation::mcmc: In MH-file number 1 I'll start at line 60001. Estimation::mcmc: Finally I keep 140000 draws per chain. MCMC Inefficiency factors per block Parameter Block 1 Block 2 SE_e_z 658.169 639.700 SE_e_b 505.658 565.177 SE_e_i 470.724 620.827 SE_e_w 564.861 423.889 SE_e_p 195.697 259.421 SE_e_x 599.790 610.127 SE_e_r 135.408 180.089 SE_e_gm 368.410 383.779 SE_e_gp 371.486 408.434 SE_e_gi 647.870 631.643 SE_e_T 666.373 535.502 SE_e_tc 69.623 69.465 SE_e_tw 736.467 740.024 SE_e_tk 67.272 70.246 dsge_prior_weight 716.526 733.405 v_gm 722.972 725.805 v_gp 733.582 738.244 v 539.570 550.173 omega 734.608 708.307 sigma 709.750 712.156 theta 709.900 690.669 chi 738.189 724.542 zetainv 705.253 712.366 mu 638.562 698.666 phi 711.625 732.711 gammaw 715.592 694.481 xiw 734.417 717.501 gammap 714.430 548.124 xip 717.841 719.004 lambdap 738.366 697.009 zstar 704.842 680.990 lstar 690.328 696.593 pistar 730.653 723.263 rstar 693.708 684.423 phi_r 735.175 696.372 phi_r_pi 717.386 731.037 phi_r_y 729.118 721.860 phi_gm 731.807 739.592 phi_gm_y 717.904 730.761 phi_gm_b 734.028 669.719 phi_gp 728.608 719.974 phi_gp_y 725.866 687.360 phi_gp_b 743.091 739.894 phi_gi 709.957 719.839 phi_gi_y 706.419 722.722 phi_gi_b 719.132 726.417 phi_T 734.136 717.433 phi_T_y 726.922 721.440 phi_T_b 701.688 648.803 phi_tc 715.955 698.505 phi_tc_y 611.100 244.922 phi_tc_b 682.841 286.077 phi_tw 720.765 705.876 phi_tw_y 680.100 721.863 phi_tw_b 722.494 694.189 phi_tk 740.121 718.644 phi_tk_y 518.177 439.538 phi_tk_b 576.529 343.513 rho_z 715.631 703.641 rho_b 658.470 720.789 rho_i 699.857 685.580 rho_w 731.975 690.318 rho_p 742.539 736.552 rho_x 722.000 689.502 rho_r 718.687 660.529 rho_gm 722.655 739.500 rho_gp 727.308 690.830 rho_gi 738.043 731.700 rho_T 676.747 721.867 Estimation::mcmc::diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998): Parameter 1... Done! Parameter 2... Done! Parameter 3... Done! Parameter 4... Done! Parameter 5... Done! Parameter 6... Done! Parameter 7... Done! Parameter 8... Done! Parameter 9... Done! Parameter 10... Done! Parameter 11... Done! Parameter 12... Done! Parameter 13... Done! Parameter 14... Done! Parameter 15... Done! Parameter 16... Done! Parameter 17... Done! Parameter 18... Done! Parameter 19... Done! Parameter 20... Done! Parameter 21... Done! Parameter 22... Done! Parameter 23... Done! Parameter 24... Done! Parameter 25... Done! Parameter 26... Done! Parameter 27... Done! Parameter 28... Done! Parameter 29... Done! Parameter 30... Done! Parameter 31... Done! Parameter 32... Done! Parameter 33... Done! Parameter 34... Done! Parameter 35... Done! Parameter 36... Done! Parameter 37... Done! Parameter 38... Done! Parameter 39... Done! Parameter 40... Done! Parameter 41... Done! Parameter 42... Done! Parameter 43... Done! Parameter 44... Done! Parameter 45... Done! Parameter 46... Done! Parameter 47... Done! Parameter 48... Done! Parameter 49... Done! Parameter 50... Done! Parameter 51... Done! Parameter 52... Done! Parameter 53... Done! Parameter 54... Done! Parameter 55... Done! Parameter 56... Done! Parameter 57... Done! Parameter 58... Done! Parameter 59... Done! Parameter 60... Done! Parameter 61... Done! Parameter 62... Done! Parameter 63... Done! Parameter 64... Done! Parameter 65... Done! Parameter 66... Done! Parameter 67... Done! Parameter 68... Done! Parameter 69... Done! Estimation::marginal density: I'm computing the posterior mean and covariance... Done! Estimation::marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... Done! ESTIMATION RESULTS Log data density is -1421.256629. parameters prior mean post. mean 90% HPD interval prior pstdev dsge_prior_weight 1.000 1.0809 0.9486 1.1986 unif 0.5774 v_gm 0.000 1.0836 0.5501 1.6952 norm 1.5000 v_gp 0.000 0.5640 0.1424 0.9720 norm 1.5000 v 0.250 0.2302 0.1837 0.2755 norm 1.0000 omega 0.250 0.1473 0.1173 0.1756 beta 0.1000 sigma 1.000 1.4648 1.3227 1.5958 gamm 0.3750 theta 0.700 0.4218 0.3404 0.5009 beta 0.1500 chi 2.000 2.2444 1.8512 2.5427 gamm 0.7500 zetainv 4.000 4.0956 3.6185 4.7292 gamm 1.5000 mu 1.000 1.1135 0.9092 1.3253 gamm 1.0000 phi 0.075 0.0725 0.0694 0.0763 gamm 0.0125 gammaw 0.500 0.3551 0.2585 0.4317 beta 0.2500 xiw 0.375 0.6080 0.5641 0.6600 beta 0.1000 gammap 0.500 0.9561 0.8928 0.9993 beta 0.2500 xip 0.375 0.6098 0.5767 0.6438 beta 0.1000 lambdap 0.150 0.2791 0.2349 0.3285 gamm 0.0500 zstar 0.190 0.1780 0.1637 0.1922 gamm 0.0500 lstar 0.000 -0.0228 -0.0351 -0.0106 norm 0.0500 pistar 0.175 0.1432 0.1329 0.1543 gamm 0.0500 rstar 0.498 0.5391 0.5229 0.5550 gamm 0.0500 phi_r 0.800 0.7650 0.7462 0.7968 beta 0.1000 phi_r_pi 0.500 0.4988 0.4294 0.5752 norm 0.2000 phi_r_y 0.125 0.0858 0.0686 0.1024 gamm 0.0500 phi_gm 0.125 0.7693 0.7485 0.7859 beta 0.0500 phi_gm_y 0.000 1.2123 0.9964 1.4297 norm 0.5000 phi_gm_b 0.000 0.7509 0.5076 0.9669 norm 0.5000 phi_gp 0.800 0.8509 0.8243 0.8747 beta 0.1000 phi_gp_y 0.000 -0.0370 -0.2569 0.1600 norm 0.5000 phi_gp_b 0.000 0.2389 0.0509 0.4622 norm 0.5000 phi_gi 0.800 0.7917 0.7700 0.8122 beta 0.1000 phi_gi_y 0.000 0.3006 0.1972 0.4173 norm 0.5000 phi_gi_b 0.000 -0.2766 -0.4559 -0.0893 norm 0.5000 phi_T 0.800 0.8873 0.8658 0.9065 beta 0.1000 phi_T_y 0.000 -0.0859 -0.1954 0.0252 norm 0.5000 phi_T_b 0.000 0.1097 -0.0804 0.2825 norm 0.5000 phi_tc 0.800 0.9413 0.8881 0.9954 beta 0.1000 phi_tc_y 0.000 0.0106 -0.0299 0.0650 norm 0.5000 phi_tc_b 0.000 -0.0442 -0.1193 0.0120 norm 0.5000 phi_tw 0.800 0.8162 0.7949 0.8369 beta 0.1000 phi_tw_y 0.000 -0.1826 -0.2665 -0.0823 norm 0.5000 phi_tw_b 0.000 0.0334 -0.0504 0.1293 norm 0.5000 phi_tk 0.800 0.8747 0.8298 0.9327 beta 0.1000 phi_tk_y 0.000 0.0247 -0.0017 0.0490 norm 0.5000 phi_tk_b 0.000 0.0412 0.0202 0.0620 norm 0.5000 rho_z 0.500 0.1747 0.1243 0.2205 beta 0.2000 rho_b 0.500 0.3251 0.2718 0.3666 beta 0.2000 rho_i 0.500 0.4034 0.3524 0.4463 beta 0.2000 rho_w 0.500 0.5654 0.4653 0.6524 beta 0.2000 rho_p 0.500 0.4810 0.3858 0.5611 beta 0.2000 rho_x 0.500 0.8357 0.7883 0.8822 beta 0.2000 rho_r 0.500 0.5607 0.5245 0.6039 beta 0.2000 rho_gm 0.500 0.4724 0.4215 0.5712 beta 0.2000 rho_gp 0.500 0.1292 0.0605 0.2143 beta 0.2000 rho_gi 0.500 0.4605 0.3932 0.5130 beta 0.2000 rho_T 0.500 0.0763 0.0197 0.1379 beta 0.2000 standard deviation of shocks prior mean post. mean 90% HPD interval prior pstdev e_z 0.100 0.8713 0.6148 1.1362 invg Inf e_b 0.100 1.4480 1.0546 1.8461 invg Inf e_i 0.100 2.0133 1.6385 2.4147 invg Inf e_w 0.100 0.5201 0.4243 0.6141 invg Inf e_p 0.100 0.2818 0.2391 0.3219 invg Inf e_x 0.100 4.1148 3.5547 4.6631 invg Inf e_r 0.100 0.2149 0.1844 0.2449 invg Inf e_gm 0.100 0.5302 0.4506 0.6063 invg Inf e_gp 0.100 0.7289 0.6288 0.8307 invg Inf e_gi 0.100 1.7593 1.4829 2.0377 invg Inf e_T 0.100 0.1027 0.0227 0.2279 invg Inf e_tc 0.100 0.0078 0.0070 0.0086 invg Inf e_tw 0.100 0.4146 0.3992 0.4391 invg Inf e_tk 0.100 0.0093 0.0083 0.0104 invg Inf Estimation::mcmc: Smoothed variables Estimation::mcmc: Smoothed variables, done! Estimation::mcmc: Smoothed shocks Estimation::mcmc: Smoothed shocks, done! Estimation::mcmc: Trend_coefficients Estimation::mcmc: Trend_coefficients, done! Estimation::mcmc: Smoothed constant Estimation::mcmc: Smoothed constant, done! Estimation::mcmc: Smoothed trend Estimation::mcmc: Smoothed trend, done! Estimation::mcmc: Updated Variables Estimation::mcmc: Updated Variables, done! MODEL SUMMARY Number of variables: 53 Number of stochastic shocks: 14 Number of state variables: 31 Number of jumpers: 11 Number of static variables: 19 MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS Variables e_b e_w e_z e_p e_i e_x e_r e_gm e_gp e_gi e_T e_tc e_tw e_tk e_b 2.096836 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_w 0.000000 0.270499 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_z 0.000000 0.000000 0.759137 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_p 0.000000 0.000000 0.000000 0.079426 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_i 0.000000 0.000000 0.000000 0.000000 4.053321 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_x 0.000000 0.000000 0.000000 0.000000 0.000000 16.931574 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_r 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.046161 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_gm 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.281137 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 e_gp 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.531339 0.000000 0.000000 0.000000 0.000000 0.000000 e_gi 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.095251 0.000000 0.000000 0.000000 0.000000 e_T 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.010542 0.000000 0.000000 0.000000 e_tc 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000061 0.000000 0.000000 e_tw 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.171925 0.000000 e_tk 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000087 POLICY AND TRANSITION FUNCTIONS dy_obs dhc_obs dhi_obs dgpc_obs dgic_obs dgi_obs dw_obs l_obs pi_obs rn_obs tax_c_obs tax_w_obs tax_k_obs Constant 0.178004 0.178004 0.178004 0.178004 0.178004 0.178004 0.178004 -0.022762 0.143158 0.682254 0.061000 0.273000 0.446000 c(-1) 0 -1.000000 0 0 0 0 0 0 0 0 0 0 0 rn(-1) 0.163722 0.225694 0.248163 0 0 0 0.330991 0.183729 0.361487 0.778867 0 0 0 k(-1) -0.067635 -0.050702 -0.176610 0 0 0 -0.110924 -0.395212 -0.183351 -0.006738 0 0 0 y(-1) -1.013395 -0.044085 -0.023038 -0.041129 0.106430 0.120276 -0.010880 -0.024236 -0.012126 -0.000626 -0.003217 0.039690 0.002063 b(-1) 0.213961 0.300991 0.259418 0.035607 0.173213 -0.057638 0.289920 0.353646 0.217942 0.010703 0.002592 -0.006133 -0.005159 k_g(-1) -0.130231 -0.203070 -0.156390 0 0 0 -0.224551 -0.560142 -0.280467 -0.010846 0 0 0 gm(-1) 0.129982 0.006834 0.092071 0 -0.230664 0.791654 0.131276 0.187160 0.092955 0.005346 0 0 0 gp(-1) 0.091027 0.037460 0.067979 -0.149052 0 0 0.094396 0.134299 0.068766 0.003851 0 0 0 gi(-1) 0 0 0 0 0 -1.000000 0 0 0 0 0 0 0 t(-1) 0.261445 0.425276 0.283294 0 0 0 0.329266 0.420991 0.246370 0.012494 0 0 0 t_c(-1) -0.784317 -1.400952 -0.631855 0 0 0 -0.874758 -1.200800 -0.655072 -0.035017 -0.058692 0 0 t_w(-1) -0.467617 -0.744097 -0.535513 0 0 0 -0.375769 -0.763958 -0.353354 -0.019787 0 -0.183785 0 z_w(-1) 0.027416 0.083175 -0.037493 0 0 0 0.445126 -0.027310 0.144501 0.004788 0 0 0 z_p(-1) -0.114565 -0.144053 -0.197824 0 0 0 -0.382981 -0.065314 0.320150 0.007072 0 0 0 z_x(-1) 0.057355 -0.029543 -0.051213 0 0 0 -0.003623 0.046695 -0.009524 0.000878 0 0 0 z_r(-1) -0.055330 -0.104680 -0.034385 0 0 0 0.054715 -0.157371 0.190059 0.565202 0 0 0 z_gm(-1) 0.084105 0.048997 0.090510 0 0.472400 0 0.117602 0.137951 0.088059 0.004277 0 0 0 z_gp(-1) 0.014991 0.007558 0.011657 0.129172 0 0 0.016074 0.022377 0.011772 0.000647 0 0 0 z_gi(-1) 0.030480 0.013647 0.005423 0 0 0.460528 0.015753 0.035615 0.008813 0.000873 0 0 0 z_T(-1) 0.023938 0.038835 0.026120 0 0 0 0.030322 0.038637 0.022706 0.001148 0 0 0 ypot(-1) -0.013000 0.034401 -0.009112 0.005522 -0.279643 -0.062638 -0.025401 -0.017684 -0.015117 -0.000705 0.000624 -0.033557 0.003097 pi(-2) -0.003007 -0.006142 -0.001079 0 0 0 0.003168 -0.009076 0.010470 0.029553 0 0 0 pi(-3) -0.000913 -0.002514 0.000801 0 0 0 0.002963 -0.004904 0.006543 0.029480 0 0 0 c_e(-1) 0.171396 0.425128 -0.069168 0 0 0 0.004038 0.157001 -0.018093 0.002927 0 0 0 pi(-1) -0.161115 -0.228805 -0.232532 0 0 0 -0.386970 -0.173168 0.523378 0.041396 0 0 0 i(-1) 0.139031 -0.105161 -0.272240 0 0 0 -0.030125 0.089745 -0.044729 0.001493 0 0 0 w(-1) 0.016988 0.055172 -0.029562 0 0 0 -0.475929 -0.089373 0.105702 0.003440 0 0 0 t_k(-1) -0.456872 -0.625453 -0.700093 0 0 0 -0.533928 -0.371186 -0.303935 -0.018122 0 0 -0.125260 z_b(-1) 0.048606 0.128008 -0.032584 0 0 0 0.039447 0.033857 0.003041 0.001069 0 0 0 z_i(-1) -0.033994 0.062048 -0.241233 0 0 0 0.024105 -0.004874 0.025364 0.000058 0 0 0 z_z(-1) 0.038472 0.028736 0.005133 0.026043 0.040303 0.036403 0.009328 -0.044812 -0.032976 -0.001446 0 0 0 e_b 0.149521 0.393773 -0.100235 0 0 0 0.121345 0.104151 0.009355 0.003290 0 0 0 e_w 0.048492 0.147116 -0.066317 0 0 0 0.787317 -0.048305 0.255587 0.008469 0 0 0 e_z 0.220186 0.164463 0.029379 0.149052 0.230664 0.208346 0.053387 -0.256473 -0.188734 -0.008277 0 0 0 e_p -0.238198 -0.299509 -0.411306 0 0 0 -0.796275 -0.135798 0.665640 0.014703 0 0 0 e_i -0.084261 0.153802 -0.597955 0 0 0 0.059750 -0.012081 0.062872 0.000143 0 0 0 e_x 0.068633 -0.035353 -0.061284 0 0 0 -0.004335 0.055876 -0.011397 0.001050 0 0 0 e_r -0.098671 -0.186679 -0.061319 0 0 0 0.097575 -0.280645 0.338939 1.007943 0 0 0 e_gm 0.178038 0.103719 0.191596 0 1.000000 0 0.248946 0.292022 0.186407 0.009054 0 0 0 e_gp 0.116058 0.058510 0.090241 1.000000 0 0 0.124436 0.173231 0.091132 0.005012 0 0 0 e_gi 0.066184 0.029633 0.011775 0 0 1.000000 0.034206 0.077336 0.019136 0.001896 0 0 0 e_T 0.313623 0.508789 0.342204 0 0 0 0.397268 0.506204 0.297486 0.015044 0 0 0 e_tc -0.833220 -1.488304 -0.671252 0 0 0 -0.929300 -1.275672 -0.695917 -0.037201 1.000000 0 0 e_tw -0.572908 -0.911643 -0.656093 0 0 0 -0.460380 -0.935976 -0.432917 -0.024243 0 1.000000 0 e_tk -0.522295 -0.715015 -0.800343 0 0 0 -0.610385 -0.424339 -0.347457 -0.020717 0 0 1.000000 THEORETICAL MOMENTS VARIABLE MEAN STD. DEV. VARIANCE dy_obs 0.1780 0.6576 0.4325 dhc_obs 0.1780 0.9545 0.9110 dhi_obs 0.1780 1.4459 2.0905 dgpc_obs 0.1780 0.8046 0.6474 dgic_obs 0.1780 0.6730 0.4530 dgi_obs 0.1780 2.1344 4.5557 dw_obs 0.1780 0.7539 0.5684 l_obs -0.0228 2.2539 5.0803 pi_obs 0.1432 1.9504 3.8041 rn_obs 0.6823 1.7331 3.0035 tax_c_obs 0.0610 0.0104 0.0001 tax_w_obs 0.2730 0.4399 0.1935 tax_k_obs 0.4460 0.0150 0.0002 VARIANCE DECOMPOSITION (in percent) e_b e_w e_z e_p e_i e_x e_r e_gm e_gp e_gi e_T e_tc e_tw e_tk dy_obs 17.46 2.31 19.32 3.11 7.68 24.00 0.46 3.13 2.16 4.10 0.32 0.01 15.93 0.01 dhc_obs 46.93 2.12 10.19 2.48 12.58 3.26 0.51 1.07 0.49 0.41 0.39 0.02 19.57 0.01 dhi_obs 1.89 1.30 2.95 1.84 80.64 5.19 0.18 0.79 0.34 0.03 0.09 0.00 4.75 0.00 dgpc_obs 0.06 0.02 11.11 0.01 0.02 0.06 0.00 0.04 88.60 0.00 0.00 0.00 0.07 0.00 dgic_obs 0.55 1.71 16.30 2.03 0.79 0.10 0.36 76.74 0.24 0.14 0.06 0.00 0.99 0.00 dgi_obs 0.10 0.13 1.86 0.15 0.05 0.06 0.03 5.16 0.02 92.34 0.01 0.00 0.11 0.00 dw_obs 8.28 40.29 12.12 16.17 4.24 0.20 0.65 4.55 2.14 0.83 0.43 0.01 10.06 0.01 l_obs 0.84 2.46 82.51 1.13 1.40 1.50 0.30 1.16 0.62 0.50 0.11 0.00 7.46 0.00 pi_obs 0.44 1.91 60.61 1.79 4.66 1.21 18.75 2.00 0.86 0.20 0.19 0.01 7.39 0.00 rn_obs 0.10 0.14 70.71 0.05 0.72 0.30 25.52 0.43 0.17 0.09 0.04 0.00 1.74 0.00 tax_c_obs 2.09 0.70 30.25 0.31 0.89 2.26 0.10 1.70 0.73 0.10 0.16 57.60 3.10 0.00 tax_w_obs 0.07 0.02 0.32 0.03 0.08 0.10 0.00 0.03 0.02 0.01 0.00 0.00 99.31 0.00 tax_k_obs 1.88 1.96 46.46 1.58 0.38 1.19 0.37 1.68 0.70 0.04 0.17 0.01 1.97 41.60 MATRIX OF CORRELATIONS Variables dy_obs dhc_obs dhi_obs dgpc_obs dgic_obs dgi_obs dw_obs l_obs pi_obs rn_obs tax_c_obs tax_w_obs tax_k_obs dy_obs 1.0000 0.5248 0.3919 0.2840 0.3051 0.2513 0.5290 0.1337 -0.1167 -0.0395 0.1007 -0.4028 -0.0289 dhc_obs 0.5248 1.0000 -0.1487 0.1604 0.1479 0.0772 0.6813 0.0876 -0.0970 -0.0458 0.0687 -0.4465 -0.0232 dhi_obs 0.3919 -0.1487 1.0000 0.1069 0.0985 0.0250 0.0503 0.0288 -0.0616 -0.0124 0.0416 -0.1986 0.0145 dgpc_obs 0.2840 0.1604 0.1069 1.0000 0.0598 0.0296 0.2369 0.0611 -0.0676 -0.0482 -0.0221 -0.0227 0.1077 dgic_obs 0.3051 0.1479 0.0985 0.0598 1.0000 0.1101 0.2822 0.0782 -0.0322 -0.0276 0.1205 0.0164 -0.1875 dgi_obs 0.2513 0.0772 0.0250 0.0296 0.1101 1.0000 0.1136 0.0570 -0.0061 -0.0109 0.0186 0.0024 -0.0120 dw_obs 0.5290 0.6813 0.0503 0.2369 0.2822 0.1136 1.0000 0.1235 -0.1426 -0.0558 0.0736 -0.2954 -0.0306 l_obs 0.1337 0.0876 0.0288 0.0611 0.0782 0.0570 0.1235 1.0000 -0.6071 -0.7403 -0.1571 -0.0878 0.0911 pi_obs -0.1167 -0.0970 -0.0616 -0.0676 -0.0322 -0.0061 -0.1426 -0.6071 1.0000 0.9072 0.0356 0.0007 -0.0494 rn_obs -0.0395 -0.0458 -0.0124 -0.0482 -0.0276 -0.0109 -0.0558 -0.7403 0.9072 1.0000 0.0682 0.0262 -0.0626 tax_c_obs 0.1007 0.0687 0.0416 -0.0221 0.1205 0.0186 0.0736 -0.1571 0.0356 0.0682 1.0000 -0.0533 -0.4217 tax_w_obs -0.4028 -0.4465 -0.1986 -0.0227 0.0164 0.0024 -0.2954 -0.0878 0.0007 0.0262 -0.0533 1.0000 0.0089 tax_k_obs -0.0289 -0.0232 0.0145 0.1077 -0.1875 -0.0120 -0.0306 0.0911 -0.0494 -0.0626 -0.4217 0.0089 1.0000 COEFFICIENTS OF AUTOCORRELATION Order 1 2 3 4 5 dy_obs 0.1727 0.0161 -0.0464 -0.0639 -0.0583 dhc_obs 0.1267 -0.0887 -0.1248 -0.1018 -0.0632 dhi_obs 0.3122 0.0387 -0.0673 -0.1028 -0.1068 dgpc_obs 0.1374 0.0202 0.0001 -0.0056 -0.0078 dgic_obs 0.3785 0.0411 -0.0819 -0.1261 -0.1315 dgi_obs -0.2176 -0.1068 -0.0578 -0.0336 -0.0216 dw_obs 0.3173 -0.0402 -0.1824 -0.1937 -0.1420 l_obs 0.9497 0.8962 0.8538 0.8259 0.8106 pi_obs 0.9672 0.9079 0.8484 0.8017 0.7702 rn_obs 0.9897 0.9708 0.9492 0.9276 0.9070 tax_c_obs 0.3797 0.3457 0.3117 0.2825 0.2593 tax_w_obs -0.0936 -0.0833 -0.0688 -0.0540 -0.0411 tax_k_obs 0.5286 0.4761 0.4214 0.3732 0.3338 {エラー: dseries/subsref (行 212) dseries::subsref: Indices are out of bounds! Subsample cannot end after 152Y. エラー: makedataset (行 253) DynareDataset = DynareDataset(FIRSTOBS:lastobs); エラー: dynare_estimation_init (行 553) [dataset_, dataset_info, newdatainterfaceflag] = makedataset(options_, options_.dsge_var*options_.dsge_varlag, gsa_flag); エラー: evaluate_smoother (行 64) [dataset_,dataset_info,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_); エラー: shock_decomposition (行 87) [oo_, M_, ~, ~, Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set, varlist, M_, oo_, options_, bayestopt_, estim_params_); エラー: jpmodel5.driver (行 847) oo_ = shock_decomposition(M_,oo_,options_,var_list_,bayestopt_,estim_params_); エラー: dynare (行 293) evalin('base',[fname '.driver']) ; }