Starting Dynare (version 6-unstable-2023-12-05-1855-3df48562). Calling Dynare with arguments: none Starting preprocessing of the model file ... Found 7 equation(s). Evaluating expressions... Computing static model derivatives (order 1). Normalizing the static model... Finding the optimal block decomposition of the static model... 3 block(s) found: 3 recursive block(s) and 0 simultaneous block(s). the largest simultaneous block has 0 equation(s) and 0 feedback variable(s). Computing dynamic model derivatives (order 2). Normalizing the dynamic model... Finding the optimal block decomposition of the dynamic model... 1 block(s) found: 1 recursive block(s) and 0 simultaneous block(s). the largest simultaneous block has 0 equation(s) and 0 feedback variable(s). Preprocessing completed. Preprocessing time: 0h00m00s. Initial value of the log posterior (or likelihood): -96.1038 Gradient norm 5.0086 Minimum Hessian eigenvalue 25.2683 Maximum Hessian eigenvalue 25.2683 Iteration 1 Predicted improvement: 0.496392459 lambda = 1; f = 95.6664472 Norm of dx 0.19822 Predicted improvement: 12.543008610 lambda = 1; f = 118.3521181 lambda = 0.33333; f = 98.0020045 lambda = 0.11111; f = 96.1738960 lambda = 0.037037; f = 96.3781940 lambda = 0.012346; f = 95.7784172 lambda = 0.0041152; f = 95.6899064 lambda = 0.0013717; f = 95.6727407 lambda = 0.00045725; f = 95.6683758 lambda = 0.00015242; f = 95.6670713 lambda = 5.0805e-05; f = 95.6666531 lambda = 1.6935e-05; f = 95.6665156 lambda = 5.645e-06; f = 95.6664699 lambda = 1.8817e-06; f = 95.6664547 lambda = 6.2723e-07; f = 95.6664497 lambda = 2.0908e-07; f = 95.6664480 lambda = 6.9692e-08; f = 95.6664474 lambda = 2.3231e-08; f = 95.6664473 lambda = 7.7435e-09; f = 95.6664472 lambda = 2.5812e-09; f = 95.6664472 lambda = -6.2723e-07 lambda = -6.2723e-07; f = 95.6664446 Norm of dx 5.0086 Predicted improvement: 0.010074789 lambda = 1; f = 95.6563083 Norm of dx 0.025026 Done for param alphaa = 0.6732 Sequence of univariate steps!! Actual dxnorm 0.17319 FVAL 95.6563 Improvement 0.44751 Ftol 1e-05 Htol 1e-05 Elapsed time for iteration 0.10083 s. Iteration 2 Predicted improvement: 0.000000783 lambda = 1; f = 95.6563078 Norm of dx 0.00025296 Sequence of univariate steps!! Try diagonal Hessian Predicted improvement: 0.000000783 lambda = 1; f = 95.6563092 lambda = 0.33333; f = 95.6563080 lambda = 0.11111; f = 95.6563078 lambda = 0.037037; f = 95.6563078 lambda = 0.012346; f = 95.6563078 lambda = 0.0041152; f = 95.6563078 lambda = 0.0013717; f = 95.6563078 lambda = 0.00045725; f = 95.6563078 lambda = 0.00015242; f = 95.6563078 lambda = 5.0805e-05; f = 95.6563078 lambda = 1.6935e-05; f = 95.6563078 lambda = 5.645e-06; f = 95.6563078 lambda = 1.8817e-06; f = 95.6563078 lambda = 6.2723e-07; f = 95.6563078 lambda = 2.0908e-07; f = 95.6563078 lambda = 6.9692e-08; f = 95.6563078 lambda = 2.3231e-08; f = 95.6563078 lambda = 7.7435e-09; f = 95.6563078 lambda = 2.5812e-09; f = 95.6563078 lambda = 4.9899e-09; f = 95.6563078 lambda = 9.6463e-09; f = 95.6563078 lambda = 1.8648e-08; f = 95.6563078 lambda = 1.2557e-08; f = 95.6563078 lambda = 1.592e-08; f = 95.6563078 lambda = 1.3807e-08; f = 95.6563078 lambda = 1.1975e-08; f = 95.6563078 lambda = 1.3043e-08; f = 95.6563078 lambda = 1.2391e-08; f = 95.6563078 lambda = 1.2778e-08; f = 95.6563078 lambda = 1.3177e-08; f = 95.6563078 Norm of dx 0.00025296 Try gradient direction Predicted improvement: 0.000000002 lambda = 1; f = 95.6563078 lambda = 0.33333; f = 95.6563078 lambda = 0.11111; f = 95.6563078 lambda = 0.037037; f = 95.6563078 lambda = 0.012346; f = 95.6563078 lambda = 0.0041152; f = 95.6563078 lambda = 0.0013717; f = 95.6563078 lambda = 0.00045725; f = 95.6563078 lambda = 0.00015242; f = 95.6563078 lambda = 5.0805e-05; f = 95.6563078 lambda = 1.6935e-05; f = 95.6563078 lambda = 5.645e-06; f = 95.6563078 lambda = 1.8817e-06; f = 95.6563078 lambda = 6.2723e-07; f = 95.6563078 lambda = 2.0908e-07; f = 95.6563078 lambda = 6.9692e-08; f = 95.6563078 lambda = 2.3231e-08; f = 95.6563078 lambda = 7.7435e-09; f = 95.6563078 lambda = 2.5812e-09; f = 95.6563078 lambda = -6.2723e-07 lambda = -6.2723e-07; f = 95.6563078 lambda = -2.0908e-07; f = 95.6563078 lambda = -6.9692e-08; f = 95.6563078 lambda = -2.3231e-08; f = 95.6563078 lambda = -7.7435e-09; f = 95.6563078 lambda = -2.5812e-09; f = 95.6563078 Norm of dx 6.1933e-07 No further improvement is possible! Actual dxnorm 0.00025296 FVAL 95.6563 Improvement 5.536e-07 Ftol 1e-05 Htol 1e-05 Gradient norm 0.0061933 Minimum Hessian eigenvalue 24.4836 Maximum Hessian eigenvalue 24.4836 Estimation successful. Final value of minus the log posterior (or likelihood):95.656308 RESULTS FROM POSTERIOR ESTIMATION parameters prior mean mode s.d. prior pstdev alphaa 0.5000 0.6729 0.1778 norm 0.5000 Log data density [Laplace approximation] is -96.464577. 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 Model1/metropolis\Model1_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: 30000. Estimation::mcmc: Current acceptance ratio per chain: Chain 1: 43.1567% Chain 2: 43.1967% Estimation::mcmc: Total number of MH draws per chain: 30000. 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 15001. Estimation::mcmc: Finally I keep 15000 draws per chain. MCMC Inefficiency factors per block Parameter Block 1 Block 2 alphaa 6.212 6.859 Convergence diagnostics results for chain 1. Geweke (1992) Convergence Tests, based on means of draws 15000 to 18000 vs 22500 to 30000 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 alphaa 0.648 0.170 0.190 0.572 0.578 0.588 Convergence diagnostics results for chain 2. Geweke (1992) Convergence Tests, based on means of draws 15000 to 18000 vs 22500 to 30000 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 alphaa 0.645 0.171 0.074 0.369 0.397 0.446 Univariate convergence diagnostic, Brooks and Gelman (1998): Parameter 1... 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 is -96.486090. parameters prior mean post. mean 90% HPD interval prior pstdev alphaa 0.500 0.6449 0.3908 0.9497 norm 0.5000 Estimation::mcmc: Posterior (dsge) IRFs... Estimation::mcmc: Posterior IRFs, done! Total computing time : 0h00m57s