Starting Dynare (version 7-unstable-2023-12-22-1019-7bf0395a). Calling Dynare with arguments: none Preprocessing time: 0h00m00s. warning: Some of the parameters have no value (lne, lnz) when using steady. If these parameters are not initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model. Note that simul, perfect_foresight_setup, and perfect_foresight_solver do not automatically call the steady state file. STEADY-STATE RESULTS: dc 0 dr 1.0101 dm 0 dpi 1 din 0 r 1.0101 h 0.318551 x 1 a 1 e 1 z 1 v 1 y 0.738904 k 5.2627 in 0.131567 c 0.607337 d 0.123151 m 0.736936 w 1.35309 q 0.035101 n 2.31958 tau 1 lambd 1.62679 ksi 1.35566 pi 1 Dataitrobs 4.02013 Dataitpiobs 0 Dataitmobs 0 Dataitinobs 0 Dataitcobs 0 Residuals of the static equations: Equation number 1: k : 0.000000 Equation number 2: 2 : 0.000000 Equation number 3: 3 : 0.000000 Equation number 4: 4 : 0.000000 Equation number 5: 5 : 0.000000 Equation number 6: 6 : 0.000000 Equation number 7: a : 0.000000 Equation number 8: 8 : 0.000000 Equation number 9: 9 : 0.000000 Equation number 10: lambd : 0.000000 Equation number 11: 11 : 0.000000 Equation number 12: y : 0.000000 Equation number 13: d : 0.000000 Equation number 14: 14 : 0.000000 Equation number 15: 15 : 0.000000 Equation number 16: 16 : 0.000000 Equation number 17: 17 : 0.000000 Equation number 18: 18 : 0.000000 Equation number 19: n : 0.000000 Equation number 20: tau : 0.000000 Equation number 21: Dataitcobs : 0.000000 Equation number 22: Dataitmobs : 0.000000 Equation number 23: Dataitinobs : 0.000000 Equation number 24: Dataitpiobs : 0.000000 Equation number 25: Dataitrobs : 0.000000 Equation number 26: din : 0.000000 Equation number 27: dc : 0.000000 Equation number 28: dpi : 0.000000 Equation number 29: dr : 0.000000 Equation number 30: dm : 0.000000 You did not declare endogenous variables after the estimation/calib_smoother command. warning: Some of the parameters have no value (lne, lnz) when using initial_estimation_checks. If these parameters are not initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model. Note that simul, perfect_foresight_setup, and perfect_foresight_solver do not automatically call the steady state file. Initial value of the log posterior (or likelihood): -9842554.4191 warning: gmhmaxlik: Unknown option (MaxIter)! warning: called from gmhmaxlik at line 59 column 13 dynare_minimize_objective at line 350 column 48 dynare_estimation_1 at line 238 column 68 dynare_estimation at line 105 column 5 driver at line 1022 column 14 dynare at line 310 column 5 ========================================================== Change in the posterior covariance matrix = 88.1912. Change in the posterior mean = 19.8486. Current mode = -136.8852 Mode improvement = 9842691.3043 New value of jscale = 0.0049628 ========================================================== ========================================================== Change in the posterior covariance matrix = 86.0764. Change in the posterior mean = 16.2282. Current mode = -260.1703 Mode improvement = 123.285 New value of jscale = 0.1934 ========================================================== ========================================================== Change in the posterior covariance matrix = 94.4345. Change in the posterior mean = 16.5382. Current mode = -296.8564 Mode improvement = 36.6861 New value of jscale = 0.05505 ========================================================== Optimal value of the scale parameter = 0.05505 Final value of minus the log posterior (or likelihood):-296.856379 MODE CHECK Fval obtained by the optimization routine: -296.856379 RESULTS FROM POSTERIOR ESTIMATION parameters prior mean mode s.d. prior pstdev gamm 0.0500 0.1000 0.0107 unif 0.0289 phip 50.0000 24.1912 1.8576 gamm 10.0000 phik 32.1400 27.6299 1.3565 gamm 2.8000 rhoa 0.7500 0.9994 0.0371 beta 0.1500 rhoe 0.7500 0.9756 0.0179 beta 0.1500 rhox 0.7500 0.4492 0.0234 beta 0.1500 rhoz 0.7500 0.8086 0.0162 beta 0.1500 rhov 0.5000 0.0186 0.0042 beta 0.1000 omegapi 1.3000 0.6974 0.2939 norm 0.3000 omegay 0.1250 -0.0104 0.0944 norm 0.1250 omegatau 0.3800 0.5644 0.0316 norm 0.0700 standard deviation of shocks prior mean mode s.d. prior pstdev epsilona 0.0100 0.0610 0.0123 invg 0.5000 epsilone 0.0100 0.0385 0.0016 invg 0.5000 epsilonx 0.0100 0.1224 0.0984 invg 0.5000 epsilonz 0.0100 0.0181 0.0040 invg 0.5000 epsilonv 0.0100 0.8460 0.1532 invg 0.5000 Log data density [Laplace approximation] is 242.133021. Estimation::mcmc: Multiple chains mode. Estimation::mcmc: Old mh-files successfully erased! 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 Model6/metropolis\Model6_mh_history_0.mat Estimation::mcmc: Number of mh files: 1 per block. Estimation::mcmc: Total number of generated files: 2. Estimation::mcmc: Total number of iterations: 250000. Estimation::mcmc: Current acceptance ratio per chain: Chain 1: 19.9496% Chain 2: 15.242% Estimation::mcmc: Total number of MH draws per chain: 250000. Estimation::mcmc: Total number of generated MH files: 1. Estimation::mcmc: I'll use mh-files 1 to 1. Estimation::mcmc: In MH-file number 1 I'll start at line 50001. Estimation::mcmc: Finally I keep 200000 draws per chain. MCMC Inefficiency factors per block Parameter Block 1 Block 2 SE_epsilona 709.911 716.244 SE_epsilone 721.371 731.269 SE_epsilonx 512.544 530.394 SE_epsilonz 702.926 697.001 SE_epsilonv 727.723 703.771 gamm 637.199 722.633 phip 737.006 734.209 phik 733.839 736.545 rhoa 143.290 122.820 rhoe 648.892 685.239 rhox 722.382 734.154 rhoz 744.365 744.079 rhov 643.065 637.172 omegapi 702.839 712.376 omegay 614.909 620.996 omegatau 742.486 731.647 Convergence diagnostics results for chain 1. Geweke (1992) Convergence Tests, based on means of draws 50000 to 90000 vs 150000 to 250000 for chain 1. p-values are for Chi2-test for equality of means. Parameter Post. Mean Post. Std p-val No Taper p-val 4% Taper p-val 8% Taper p-val 15% Taper SE_epsilona 0.059 0.005 0.000 0.283 0.409 0.494 SE_epsilone 0.038 0.002 0.503 0.987 0.990 0.992 SE_epsilonx 0.138 0.015 0.034 0.953 0.964 0.972 SE_epsilonz 0.020 0.002 0.000 0.000 0.002 0.005 SE_epsilonv 0.919 0.092 0.000 0.000 0.000 0.000 gamm 0.098 0.002 0.000 0.005 0.019 0.037 phip 22.009 3.105 0.000 0.018 0.083 0.172 phik 28.708 0.633 0.000 0.819 0.868 0.897 rhoa 0.998 0.001 0.000 0.008 0.021 0.029 rhoe 0.974 0.010 0.000 0.009 0.047 0.104 rhox 0.550 0.034 0.000 0.000 0.000 0.000 rhoz 0.838 0.019 0.000 0.000 0.000 0.000 rhov 0.017 0.003 0.000 0.002 0.018 0.053 omegapi 0.721 0.094 0.000 0.089 0.200 0.296 omegay -0.014 0.006 0.000 0.000 0.006 0.027 omegatau 0.620 0.037 0.000 0.006 0.044 0.118 Convergence diagnostics results for chain 2. Geweke (1992) Convergence Tests, based on means of draws 50000 to 90000 vs 150000 to 250000 for chain 2. p-values are for Chi2-test for equality of means. Parameter Post. Mean Post. Std p-val No Taper p-val 4% Taper p-val 8% Taper p-val 15% Taper SE_epsilona 0.061 0.005 0.000 0.000 0.000 0.000 SE_epsilone 0.039 0.002 0.170 0.975 0.981 0.985 SE_epsilonx 0.127 0.014 0.000 0.003 0.028 0.084 SE_epsilonz 0.018 0.001 0.000 0.025 0.070 0.094 SE_epsilonv 0.855 0.066 0.000 0.000 0.006 0.021 gamm 0.097 0.003 0.612 0.991 0.993 0.994 phip 22.640 2.359 0.000 0.000 0.000 0.004 phik 27.831 0.636 0.000 0.008 0.051 0.128 rhoa 0.999 0.001 0.000 0.025 0.073 0.119 rhoe 0.976 0.011 0.000 0.845 0.879 0.899 rhox 0.451 0.027 0.000 0.000 0.000 0.000 rhoz 0.855 0.024 0.000 0.000 0.000 0.000 rhov 0.017 0.002 0.000 0.000 0.000 0.001 omegapi 0.758 0.092 0.000 0.246 0.383 0.483 omegay -0.012 0.005 0.000 0.586 0.682 0.740 omegatau 0.560 0.018 0.000 0.890 0.918 0.934 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! marginal density: I'm computing the posterior mean and covariance... Done! marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... Done! ESTIMATION RESULTS Log data density (Modified Harmonic Mean) is 247.657706. parameters prior mean post. mean 90% HPD interval prior pstdev gamm 0.050 0.0974 0.0937 0.1000 unif 0.0289 phip 50.000 21.8642 16.9475 26.0020 gamm 10.0000 phik 32.140 28.2770 26.8865 29.6021 gamm 2.8000 rhoa 0.750 0.9985 0.9971 0.9999 beta 0.1500 rhoe 0.750 0.9766 0.9616 0.9917 beta 0.1500 rhox 0.750 0.5048 0.4104 0.5874 beta 0.1500 rhoz 0.750 0.8543 0.8189 0.8821 beta 0.1500 rhov 0.500 0.0168 0.0129 0.0204 beta 0.1000 omegapi 1.300 0.7335 0.5654 0.8803 norm 0.3000 omegay 0.125 -0.0124 -0.0209 -0.0034 norm 0.1250 omegatau 0.380 0.5940 0.5378 0.6725 norm 0.0700 standard deviation of shocks prior mean post. mean 90% HPD interval prior pstdev epsilona 0.010 0.0601 0.0522 0.0678 invg 0.5000 epsilone 0.010 0.0384 0.0354 0.0417 invg 0.5000 epsilonx 0.010 0.1323 0.1065 0.1584 invg 0.5000 epsilonz 0.010 0.0189 0.0164 0.0213 invg 0.5000 epsilonv 0.010 0.8814 0.7370 1.0252 invg 0.5000 warning: Some of the parameters have no value (lne, lnz) when using initial_estimation_checks. If these parameters are not initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model. Note that simul, perfect_foresight_setup, and perfect_foresight_solver do not automatically call the steady state file. Initial value of the log posterior (or likelihood): -9842554.4191 warning: gmhmaxlik: Unknown option (MaxIter)! warning: called from gmhmaxlik at line 59 column 13 dynare_minimize_objective at line 350 column 48 dynare_estimation_1 at line 238 column 68 reestimate at line 13 column 1 driver at line 1025 column 15 dynare at line 310 column 5 ========================================================== Change in the posterior covariance matrix = 92.1936. Change in the posterior mean = 18.3163. Current mode = -288.0146 Mode improvement = 9842842.4337 New value of jscale = 0.0055404 ========================================================== ========================================================== Change in the posterior covariance matrix = 1.5434. Change in the posterior mean = 50.3173. Current mode = -331.8352 Mode improvement = 43.8207 New value of jscale = 0.10717 ========================================================== ========================================================== Change in the posterior covariance matrix = 7.6158. Change in the posterior mean = 34.2686. Current mode = 135.4055 Mode improvement = 467.2407 New value of jscale = 0.89048 ========================================================== Optimal value of the scale parameter = 0.89048 Final value of minus the log posterior (or likelihood):135.405461 MODE CHECK Fval obtained by the optimization routine: 135.405461 RESULTS FROM POSTERIOR ESTIMATION parameters prior mean mode s.d. prior pstdev gamm 0.0500 0.1000 0.0008 unif 0.0289 phip 50.0000 7.1774 0.5809 gamm 10.0000 phik 32.1400 28.4083 0.1718 gamm 2.8000 rhoa 0.7500 0.8165 0.0009 beta 0.1500 rhoe 0.7500 0.9268 0.0044 beta 0.1500 rhox 0.7500 0.7268 0.0026 beta 0.1500 rhoz 0.7500 0.9905 0.0043 beta 0.1500 rhov 0.5000 0.2537 0.0032 beta 0.1000 omegapi 1.3000 0.6287 0.0032 norm 0.3000 omegay 0.1250 0.4021 0.0046 norm 0.1250 omegatau 0.3800 0.7454 0.0036 norm 0.0700 standard deviation of shocks prior mean mode s.d. prior pstdev epsilona 0.0100 0.1093 0.0021 invg 0.5000 epsilone 0.0100 0.0416 0.0003 invg 0.5000 epsilonx 0.0100 0.3634 0.0032 invg 0.5000 epsilonz 0.0100 0.0187 0.0009 invg 0.5000 epsilonv 0.0100 0.8618 0.0055 invg 0.5000 Log data density [Laplace approximation] is -235.890874. Estimation::mcmc: Multiple chains mode. Estimation::mcmc: Old mh-files successfully erased! 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 Model6/metropolis\Model6_mh_history_0.mat Estimation::mcmc: Number of mh files: 1 per block. Estimation::mcmc: Total number of generated files: 2. Estimation::mcmc: Total number of iterations: 250000. Estimation::mcmc: Current acceptance ratio per chain: Chain 1: 5.9656% Chain 2: 8.3724% Estimation::mcmc: Total number of MH draws per chain: 250000. Estimation::mcmc: Total number of generated MH files: 1. Estimation::mcmc: I'll use mh-files 1 to 1. Estimation::mcmc: In MH-file number 1 I'll start at line 50001. Estimation::mcmc: Finally I keep 200000 draws per chain. MCMC Inefficiency factors per block Parameter Block 1 Block 2 SE_epsilona 720.497 744.143 SE_epsilone 729.571 692.355 SE_epsilonx 718.907 739.276 SE_epsilonz 669.514 730.273 SE_epsilonv 722.218 723.167 gamm 673.479 628.899 phip 705.155 642.219 phik 724.808 693.449 rhoa 730.137 736.984 rhoe 737.352 734.858 rhox 716.686 709.035 rhoz 454.023 638.328 rhov 745.007 746.397 omegapi 723.424 725.917 omegay 739.896 742.338 omegatau 724.759 713.626 Convergence diagnostics results for chain 1. Geweke (1992) Convergence Tests, based on means of draws 50000 to 90000 vs 150000 to 250000 for chain 1. p-values are for Chi2-test for equality of means. Parameter Post. Mean Post. Std p-val No Taper p-val 4% Taper p-val 8% Taper p-val 15% Taper SE_epsilona 0.074 0.014 0.000 0.000 0.001 0.010 SE_epsilone 0.040 0.003 0.000 0.021 0.084 0.168 SE_epsilonx 0.356 0.006 0.000 0.869 0.902 0.921 SE_epsilonz 0.007 0.003 0.000 0.186 0.297 0.363 SE_epsilonv 0.861 0.053 0.000 0.000 0.001 0.005 gamm 0.098 0.002 0.000 0.532 0.630 0.686 phip 7.825 1.366 0.000 0.220 0.339 0.411 phik 28.439 0.537 0.856 0.997 0.998 0.998 rhoa 0.823 0.006 0.000 0.000 0.000 0.002 rhoe 0.929 0.015 0.000 0.000 0.000 0.000 rhox 0.747 0.020 0.000 0.006 0.023 0.050 rhoz 0.997 0.002 0.000 0.747 0.789 0.805 rhov 0.225 0.015 0.000 0.000 0.000 0.000 omegapi 0.539 0.040 0.000 0.000 0.000 0.000 omegay 0.350 0.024 0.000 0.000 0.000 0.000 omegatau 0.795 0.026 0.000 0.000 0.005 0.030 Convergence diagnostics results for chain 2. Geweke (1992) Convergence Tests, based on means of draws 50000 to 90000 vs 150000 to 250000 for chain 2. p-values are for Chi2-test for equality of means. Parameter Post. Mean Post. Std p-val No Taper p-val 4% Taper p-val 8% Taper p-val 15% Taper SE_epsilona 0.073 0.015 0.000 0.000 0.000 0.000 SE_epsilone 0.041 0.002 0.007 0.946 0.959 0.967 SE_epsilonx 0.369 0.011 0.000 0.000 0.000 0.000 SE_epsilonz 0.009 0.005 0.000 0.000 0.000 0.000 SE_epsilonv 0.864 0.058 0.000 0.000 0.000 0.000 gamm 0.099 0.001 0.000 0.002 0.013 0.029 phip 7.203 1.084 0.000 0.411 0.507 0.568 phik 28.904 0.450 0.000 0.023 0.079 0.148 rhoa 0.830 0.007 0.000 0.100 0.235 0.366 rhoe 0.938 0.014 0.000 0.000 0.000 0.000 rhox 0.717 0.028 0.000 0.000 0.003 0.017 rhoz 0.996 0.003 0.000 0.000 0.000 0.000 rhov 0.212 0.025 0.000 0.000 0.000 0.000 omegapi 0.531 0.046 0.000 0.000 0.000 0.000 omegay 0.351 0.024 0.000 0.000 0.000 0.000 omegatau 0.806 0.029 0.000 0.000 0.000 0.002 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! marginal density: I'm computing the posterior mean and covariance... Done! marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... marginal density: The support of the weighting density function is not large enough... marginal density: I increase the variance of this distribution. marginal density: Let me try again. marginal density: Let me try again. marginal density: Let me try again. Done! ESTIMATION RESULTS Log data density (Modified Harmonic Mean) is -145.807897. parameters prior mean post. mean 90% HPD interval prior pstdev gamm 0.050 0.0988 0.0972 0.1000 unif 0.0289 phip 50.000 7.4537 5.3958 9.5570 gamm 10.0000 phik 32.140 28.6751 27.7146 29.5100 gamm 2.8000 rhoa 0.750 0.8282 0.8185 0.8392 beta 0.1500 rhoe 0.750 0.9337 0.9091 0.9582 beta 0.1500 rhox 0.750 0.7373 0.6946 0.7712 beta 0.1500 rhoz 0.750 0.9972 0.9949 0.9995 beta 0.1500 rhov 0.500 0.2112 0.1860 0.2448 beta 0.1000 omegapi 1.300 0.5202 0.4654 0.5660 norm 0.3000 omegay 0.125 0.3411 0.3232 0.3724 norm 0.1250 omegatau 0.380 0.8061 0.7626 0.8484 norm 0.0700 standard deviation of shocks prior mean post. mean 90% HPD interval prior pstdev epsilona 0.010 0.0676 0.0556 0.0783 invg 0.5000 epsilone 0.010 0.0401 0.0362 0.0441 invg 0.5000 epsilonx 0.010 0.3629 0.3467 0.3832 invg 0.5000 epsilonz 0.010 0.0061 0.0023 0.0104 invg 0.5000 epsilonv 0.010 0.8539 0.7641 0.9386 invg 0.5000 Total computing time : 11h44m00s Note: 3 warning(s) encountered in the preprocessor Note: warning(s) encountered in MATLAB/Octave code