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simple_example.log
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Starting Dynare (version 5.3).
Calling Dynare with arguments: none
Starting preprocessing of the model file ...
Substitution of endo leads >= 2: added 1 auxiliary variables and equations.
Found 15 equation(s).
Evaluating expressions...done
Computing static model derivatives (order 1).
Computing dynamic model derivatives (order 2).
Processing outputs ...
done
Preprocessing completed.
STEADY-STATE RESULTS:
m 1.011
P 2.25815
c 0.447711
e 1
W 4.5959
R 1.02121
k 5.80122
d 0.849425
n 0.187216
l 0.860425
gy_obs 1.003
gp_obs 1.00797
y 0.580765
dA 1.003
EIGENVALUES:
Modulus Real Imaginary
0 0 0
3.88e-17 -3.88e-17 0
0.7 0.7 0
0.9467 0.9467 0
1.067 1.067 0
1.091e+15 1.091e+15 0
3.91e+17 -3.91e+17 0
There are 3 eigenvalue(s) larger than 1 in modulus
for 3 forward-looking variable(s)
The rank condition is verified.
MODEL SUMMARY
Number of variables: 15
Number of stochastic shocks: 2
Number of state variables: 4
Number of jumpers: 3
Number of static variables: 9
MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS
Variables e_a e_m
e_a 0.000196 0.000000
e_m 0.000000 0.000025
POLICY AND TRANSITION FUNCTIONS
m P c e W R k d n l gy_obs gp_obs y dA
Constant 1.011000 2.258696 0.447603 1.000000 4.596381 1.021199 5.801499 0.849902 0.187300 0.860902 1.003307 1.008213 0.580940 1.003005
(correction) 0 0.000542 -0.000107 0 0.000477 -0.000013 0.000283 0.000477 0.000084 0.000477 0.000303 0.000242 0.000175 0
m(-1) 0.700000 2.535643 -0.192739 0 3.079790 0.494949 0.155122 -0.206595 -0.018099 0.493405 -0.064965 2.128839 -0.037617 0
k(-1) 0 -0.203515 0.040350 0 -0.062798 0 0.946669 -0.062798 -0.011106 -0.062798 0.017191 -0.090843 0.009954 0
y(-1) 0 0 0 0 0 0 0 0 0 0 -1.727040 0 0 0
P(-1) 0 0 0 0 0 0 0 0 0 0 0 -0.446370 0 0
e_a 0 1.180637 -0.234078 1.000000 0.364308 0 -5.491829 0.364308 0.064428 0.364308 0.903277 -0.480971 -0.057745 1.003005
e_m 1.011000 3.662193 -0.278370 0 4.448097 0.714848 0.224041 -0.298382 -0.026140 0.712618 -0.093829 1.634692 -0.054329 0
m(-1),m(-1) -0.103858 0.186156 0.133523 0 -0.473786 -0.124839 -0.109526 0.013808 0.011835 -0.090049 0.041444 1.202614 0.023997 0
k(-1),m(-1) 0 -0.223383 -0.018390 0 -0.033382 0 0.018197 -0.033382 0.001291 -0.033382 -0.000332 -0.189566 -0.000192 0
k(-1),k(-1) 0 0.024911 -0.001302 0 0.005684 0 -0.000370 0.005684 0.000854 0.005684 -0.002889 0.011119 -0.001673 0
y(-1),m(-1) 0 0 0 0 0 0 0 0 0 0 0.111862 0 0 0
y(-1),k(-1) 0 0 0 0 0 0 0 0 0 0 -0.029600 0 0 0
y(-1),y(-1) 0 0 0 0 0 0 0 0 0 0 2.973732 0 0 0
P(-1),m(-1) 0 0 0 0 0 0 0 0 0 0 0 -0.942734 0 0
P(-1),k(-1) 0 0 0 0 0 0 0 0 0 0 0 0.040229 0 0
P(-1),P(-1) 0 0 0 0 0 0 0 0 0 0 0 0.197670 0 0
e_a,e_a 0 0.248024 0.073209 0.500000 0.009149 0 2.733449 0.009149 -0.003489 0.009149 0.354415 0.087696 -0.027422 0.501502
e_m,e_a 0 1.871641 0.154081 0 0.279698 0 -0.152469 0.279698 -0.010819 0.279698 -0.091045 -0.799248 0.001612 0
e_m,e_m 0.505500 3.004166 0.079688 0 2.188913 0.250197 -0.068437 -0.184327 0.006015 0.321173 0.019431 1.340969 0.011251 0
m(-1),e_a 0 1.295894 0.106683 0 0.193658 0 -0.105567 0.193658 -0.007491 0.193658 -0.063038 -1.029124 0.001116 0
m(-1),e_m 0.700000 4.160072 0.110350 0 3.031135 0.346465 -0.094770 -0.255250 0.008330 0.444750 0.026907 3.473836 0.015580 0
k(-1),e_a 0 -0.085508 -0.025239 0 -0.003154 0 -0.942371 -0.003154 0.001203 -0.003154 0.033518 0.052675 0.009454 0
k(-1),e_m 0 -0.322629 -0.026560 0 -0.048214 0 0.026282 -0.048214 0.001865 -0.048214 -0.000480 -0.144012 -0.000278 0
y(-1),e_a 0 0 0 0 0 0 0 0 0 0 -1.555323 0 0 0
y(-1),e_m 0 0 0 0 0 0 0 0 0 0 0.161560 0 0 0
P(-1),e_a 0 0 0 0 0 0 0 0 0 0 0 0.212993 0 0
P(-1),e_m 0 0 0 0 0 0 0 0 0 0 0 -0.723906 0 0
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15770.1263
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 10 -1.577013e+04 0.000e+00 3.134e+05
1 25 -1.639835e+04 0.000e+00 2.080e+05 2.322e-02
2 36 -1.643344e+04 0.000e+00 1.095e+05 2.158e-02
3 46 -1.703949e+04 0.000e+00 1.261e+05 8.033e-02
4 56 -1.782740e+04 0.000e+00 1.381e+05 5.676e-02
5 67 -1.834938e+04 0.000e+00 5.538e+04 1.982e-01
6 78 -1.877974e+04 0.000e+00 3.403e+05 2.186e-01
7 90 -1.887749e+04 0.000e+00 9.242e+04 9.818e-02
8 101 -1.900142e+04 0.000e+00 9.164e+04 2.571e-01
9 112 -1.901736e+04 0.000e+00 4.613e+04 1.294e-01
10 122 -1.906863e+04 0.000e+00 5.691e+04 1.642e-01
11 133 -1.910461e+04 0.000e+00 5.015e+04 3.899e-02
12 144 -1.911875e+04 0.000e+00 4.871e+04 2.667e-02
13 157 -1.912552e+04 0.000e+00 3.161e+04 1.062e-02
14 168 -1.912603e+04 0.000e+00 2.256e+04 2.074e-02
15 181 -1.912808e+04 0.000e+00 1.371e+04 3.225e-02
16 193 -1.912993e+04 0.000e+00 2.041e+04 2.113e-02
17 203 -1.913937e+04 0.000e+00 7.741e+03 9.948e-02
18 214 -1.914035e+04 0.000e+00 6.571e+03 1.463e-01
19 226 -1.914361e+04 0.000e+00 5.965e+03 7.217e-02
20 238 -1.914809e+04 0.000e+00 1.172e+04 4.372e-02
21 251 -1.914890e+04 0.000e+00 9.639e+03 3.782e-02
22 263 -1.914954e+04 0.000e+00 7.026e+03 4.843e-02
23 275 -1.914962e+04 0.000e+00 3.868e+03 1.374e-02
24 285 -1.915008e+04 0.000e+00 1.662e+03 1.936e-02
25 298 -1.915018e+04 0.000e+00 9.066e+02 4.885e-03
26 309 -1.915020e+04 0.000e+00 1.828e+03 1.100e-02
27 319 -1.915024e+04 0.000e+00 2.631e+02 6.480e-03
28 329 -1.915024e+04 0.000e+00 1.211e+01 7.283e-04
29 339 -1.915024e+04 0.000e+00 8.173e+00 5.048e-05
30 349 -1.915024e+04 0.000e+00 2.865e-01 3.256e-06
First-order Norm of
Iter F-count f(x) Feasibility optimality step
31 359 -1.915024e+04 0.000e+00 1.368e+01 4.873e-04
32 369 -1.915024e+04 0.000e+00 1.522e+00 7.932e-05
33 379 -1.915024e+04 0.000e+00 2.023e-02 1.912e-06
<a href = "matlab: helpview('optim','local_min_poss_with_constr','CSHelpWindow');">Local minimum possible. Constraints satisfied</a>.
fmincon stopped because the <a href = "matlab: helpview('optim','norm_curr_step_simple_fminconip','CSHelpWindow');">size of the current step</a> is less than
the value of the <a href = "matlab: helpview('optim','step_size_tol','CSHelpWindow');">step size tolerance</a> and constraints are
satisfied to within the value of the <a href = "matlab: helpview('optim','constraint_tolerance','CSHelpWindow');">constraint tolerance</a>.
<<a href = "matlab: createExitMsg({'optimlib:sqpLineSearch:Exit2basic','fmincon'},{'optimlib:sqpLineSearch:Exit2detailed','1.000000e-06','0.000000e+00','1.000000e-06'},true,true);;">stopping criteria details</a>>
Final value of minus the log posterior (or likelihood):-19150.238273
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alp 0.3560 0.3510 0.0179 beta 0.0200
bet 0.9930 0.9924 0.0019 beta 0.0020
gam 0.0085 0.0032 0.0003 norm 0.0030
mst 1.0002 1.0113 0.0003 norm 0.0070
rho 0.1290 0.7057 0.0159 beta 0.1000
psi 0.6500 0.6705 0.0487 beta 0.0500
del 0.0100 0.0099 0.0034 beta 0.0050
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_a 0.0354 0.0152 0.0007 invg Inf
e_m 0.0089 0.0048 0.0001 invg Inf
Log data density [Laplace approximation] is 19100.576171.
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 simple_example/metropolis\simple_example_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: 3000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 76.9%
Chain 2: 76%
Estimation::mcmc: Total number of MH draws per chain: 3000.
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 1501.
Estimation::mcmc: Finally I keep 1500 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_e_a 66.332 110.691
SE_e_m 21.267 85.265
alp 108.330 65.847
bet 41.858 92.923
gam 27.259 28.540
mst 97.995 116.008
rho 45.164 25.221
psi 59.781 43.104
del 162.441 159.143
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!
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 19099.108053.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alp 0.356 0.3505 0.3231 0.3766 beta 0.0200
bet 0.993 0.9917 0.9885 0.9944 beta 0.0020
gam 0.009 0.0032 0.0029 0.0036 norm 0.0030
mst 1.000 1.0113 1.0108 1.0118 norm 0.0070
rho 0.129 0.6947 0.6740 0.7160 beta 0.1000
psi 0.650 0.6862 0.6159 0.7494 beta 0.0500
del 0.010 0.0133 0.0078 0.0202 beta 0.0050
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_a 0.035 0.0152 0.0141 0.0161 invg Inf
e_m 0.009 0.0048 0.0047 0.0050 invg Inf
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15843.6446
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 9 -1.584364e+04 0.000e+00 3.206e+05
1 23 -1.647095e+04 0.000e+00 2.075e+05 2.319e-02
2 33 -1.647874e+04 0.000e+00 1.114e+05 2.448e-02
3 43 -1.815881e+04 0.000e+00 1.683e+05 2.469e-01
4 53 -1.857768e+04 0.000e+00 1.628e+05 1.057e-01
5 63 -1.905609e+04 0.000e+00 5.333e+04 1.853e-01
6 74 -1.908206e+04 0.000e+00 5.980e+04 3.811e-02
7 86 -1.908364e+04 0.000e+00 5.202e+04 1.387e-02
8 97 -1.909083e+04 0.000e+00 7.869e+04 8.208e-02
9 109 -1.910508e+04 0.000e+00 5.368e+04 2.918e-02
10 121 -1.910778e+04 0.000e+00 4.094e+04 2.740e-02
11 130 -1.911115e+04 0.000e+00 3.832e+04 3.861e-02
12 141 -1.911190e+04 0.000e+00 2.224e+04 6.992e-03
13 152 -1.911452e+04 0.000e+00 1.715e+04 2.587e-02
14 162 -1.911564e+04 0.000e+00 2.165e+04 1.598e-02
15 171 -1.912461e+04 0.000e+00 1.342e+04 2.711e-01
16 183 -1.912465e+04 0.000e+00 7.905e+03 4.591e-02
17 194 -1.912693e+04 0.000e+00 1.063e+04 5.407e-02
18 203 -1.913020e+04 0.000e+00 4.922e+03 3.102e-02
19 218 -1.913037e+04 0.000e+00 1.290e+03 2.561e-02
20 229 -1.913050e+04 0.000e+00 1.408e+03 1.067e-02
21 244 -1.913062e+04 0.000e+00 1.686e+03 1.101e-02
22 254 -1.913085e+04 0.000e+00 1.226e+03 6.284e-03
23 263 -1.913103e+04 0.000e+00 3.925e+02 3.721e-03
24 272 -1.913103e+04 0.000e+00 4.718e+01 8.581e-03
25 281 -1.913104e+04 0.000e+00 1.265e+01 4.532e-03
26 290 -1.913104e+04 0.000e+00 4.211e+00 1.669e-04
27 299 -1.913104e+04 0.000e+00 1.471e-01 1.417e-05
28 308 -1.913104e+04 0.000e+00 4.099e+00 7.779e-04
29 317 -1.913104e+04 0.000e+00 2.995e-01 1.699e-06
30 326 -1.913104e+04 0.000e+00 5.808e-02 2.545e-06
First-order Norm of
Iter F-count f(x) Feasibility optimality step
31 335 -1.913104e+04 0.000e+00 6.082e-02 1.290e-06
32 344 -1.913104e+04 0.000e+00 2.472e-03 5.635e-07
<a href = "matlab: helpview('optim','local_min_poss_with_constr','CSHelpWindow');">Local minimum possible. Constraints satisfied</a>.
fmincon stopped because the <a href = "matlab: helpview('optim','norm_curr_step_simple_fminconip','CSHelpWindow');">size of the current step</a> is less than
the value of the <a href = "matlab: helpview('optim','step_size_tol','CSHelpWindow');">step size tolerance</a> and constraints are
satisfied to within the value of the <a href = "matlab: helpview('optim','constraint_tolerance','CSHelpWindow');">constraint tolerance</a>.
<<a href = "matlab: createExitMsg({'optimlib:sqpLineSearch:Exit2basic','fmincon'},{'optimlib:sqpLineSearch:Exit2detailed','1.000000e-06','0.000000e+00','1.000000e-06'},true,true);;">stopping criteria details</a>>
Final value of minus the log posterior (or likelihood):-19131.041785
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alp 0.3560 0.3952 0.0163 beta 0.0200
bet 0.9930 0.9915 0.0021 beta 0.0020
gam 0.0085 0.0032 0.0003 norm 0.0030
mst 1.0002 1.0112 0.0003 norm 0.0070
rho 0.1290 0.6780 0.0153 beta 0.1000
psi 0.6500 0.7273 0.0433 beta 0.0500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_a 0.0354 0.0169 0.0007 invg Inf
e_m 0.0089 0.0051 0.0001 invg Inf
Log data density [Laplace approximation] is 19086.449519.
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 simple_example/metropolis\simple_example_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: 3000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 77.6%
Chain 2: 78.9%
Estimation::mcmc: Total number of MH draws per chain: 3000.
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 1501.
Estimation::mcmc: Finally I keep 1500 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_e_a 45.741 56.428
SE_e_m 16.421 222.118
alp 38.044 81.158
bet 17.571 64.452
gam 123.289 163.015
mst 110.093 52.790
rho 117.770 183.052
psi 21.763 50.474
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!
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 19085.564216.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alp 0.356 0.3961 0.3645 0.4203 beta 0.0200
bet 0.993 0.9911 0.9877 0.9941 beta 0.0020
gam 0.009 0.0032 0.0025 0.0037 norm 0.0030
mst 1.000 1.0113 1.0107 1.0117 norm 0.0070
rho 0.129 0.6764 0.6478 0.7010 beta 0.1000
psi 0.650 0.7240 0.6636 0.7843 beta 0.0500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_a 0.035 0.0170 0.0160 0.0181 invg Inf
e_m 0.009 0.0051 0.0050 0.0053 invg Inf
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15878.8223
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 9 -1.587882e+04 0.000e+00 3.298e+05
1 23 -1.651311e+04 0.000e+00 2.061e+05 2.318e-02
2 33 -1.653856e+04 0.000e+00 1.113e+05 2.661e-02
3 43 -1.804485e+04 0.000e+00 1.735e+05 3.376e-01
4 53 -1.857665e+04 0.000e+00 1.082e+05 1.707e-01
5 63 -1.902486e+04 0.000e+00 4.653e+04 1.216e-01
6 79 -1.905016e+04 0.000e+00 1.177e+04 5.941e-03
7 89 -1.905362e+04 0.000e+00 1.494e+04 1.694e-02
8 101 -1.906270e+04 0.000e+00 2.851e+04 8.153e-02
9 112 -1.907915e+04 0.000e+00 3.281e+04 9.737e-02
10 123 -1.908904e+04 0.000e+00 7.202e+04 8.890e-02
11 133 -1.909117e+04 0.000e+00 6.749e+04 2.563e-02
12 144 -1.909235e+04 0.000e+00 7.735e+04 2.552e-02
13 155 -1.909440e+04 0.000e+00 5.990e+04 1.945e-02
14 166 -1.909441e+04 0.000e+00 4.357e+04 1.170e-02
15 183 -1.909442e+04 0.000e+00 2.922e+03 1.009e-03
16 193 -1.909445e+04 0.000e+00 3.816e+03 1.354e-03
17 203 -1.909467e+04 0.000e+00 1.320e+03 1.490e-02
18 215 -1.909468e+04 0.000e+00 8.185e+02 1.868e-02
19 234 -1.909472e+04 0.000e+00 2.334e+02 1.139e-04
20 244 -1.909472e+04 0.000e+00 4.742e+02 6.229e-04
21 253 -1.909473e+04 0.000e+00 5.226e+01 5.317e-03
22 262 -1.909474e+04 0.000e+00 3.822e+01 8.666e-04
23 271 -1.909474e+04 0.000e+00 1.835e+01 1.412e-04
24 280 -1.909474e+04 0.000e+00 1.480e+00 2.106e-05
25 289 -1.909474e+04 0.000e+00 9.442e+00 6.743e-04
26 298 -1.909474e+04 0.000e+00 2.250e+00 4.836e-05
27 307 -1.909474e+04 0.000e+00 2.946e-02 3.506e-06
28 316 -1.909474e+04 0.000e+00 1.177e+00 1.248e-04
29 325 -1.909474e+04 0.000e+00 4.000e-03 2.069e-06
30 334 -1.909474e+04 0.000e+00 6.153e-01 3.179e-05
First-order Norm of
Iter F-count f(x) Feasibility optimality step
31 343 -1.909474e+04 0.000e+00 1.068e-02 1.619e-06
32 352 -1.909474e+04 0.000e+00 2.946e-01 2.177e-06
33 363 -1.909474e+04 0.000e+00 2.624e-01 1.105e-06
<a href = "matlab: helpview('optim','local_min_poss_with_constr','CSHelpWindow');">Local minimum possible. Constraints satisfied</a>.
fmincon stopped because the <a href = "matlab: helpview('optim','norm_curr_step_simple_fminconip','CSHelpWindow');">size of the current step</a> is less than
the value of the <a href = "matlab: helpview('optim','step_size_tol','CSHelpWindow');">step size tolerance</a> and constraints are
satisfied to within the value of the <a href = "matlab: helpview('optim','constraint_tolerance','CSHelpWindow');">constraint tolerance</a>.
<<a href = "matlab: createExitMsg({'optimlib:sqpLineSearch:Exit2basic','fmincon'},{'optimlib:sqpLineSearch:Exit2detailed','1.000000e-06','0.000000e+00','1.000000e-06'},true,true);;">stopping criteria details</a>>
Final value of minus the log posterior (or likelihood):-19094.736002
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alp 0.3560 0.4226 0.0158 beta 0.0200
bet 0.9930 0.9924 0.0021 beta 0.0020
gam 0.0085 0.0032 0.0003 norm 0.0030
mst 1.0002 1.0112 0.0003 norm 0.0070
rho 0.1290 0.6562 0.0153 beta 0.1000
psi 0.6500 0.7306 0.0430 beta 0.0500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_a 0.0354 0.0184 0.0007 invg Inf
e_m 0.0089 0.0053 0.0001 invg Inf
Log data density [Laplace approximation] is 19050.270428.
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 simple_example/metropolis\simple_example_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: 3000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 78.0667%
Chain 2: 79%
Estimation::mcmc: Total number of MH draws per chain: 3000.
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 1501.
Estimation::mcmc: Finally I keep 1500 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_e_a 64.584 60.197
SE_e_m 29.542 244.063
alp 35.585 100.502
bet 17.136 23.270
gam 135.045 178.562
mst 93.912 45.906
rho 127.629 153.237
psi 40.924 57.417
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!
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 19048.967529.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alp 0.356 0.4237 0.3980 0.4514 beta 0.0200
bet 0.993 0.9923 0.9894 0.9952 beta 0.0020
gam 0.009 0.0033 0.0026 0.0038 norm 0.0030
mst 1.000 1.0113 1.0107 1.0117 norm 0.0070
rho 0.129 0.6548 0.6274 0.6814 beta 0.1000
psi 0.650 0.7272 0.6720 0.7897 beta 0.0500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_a 0.035 0.0185 0.0174 0.0195 invg Inf
e_m 0.009 0.0054 0.0052 0.0055 invg Inf
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15899.5636
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 9 -1.589956e+04 0.000e+00 3.386e+05
1 23 -1.654135e+04 0.000e+00 2.047e+05 2.318e-02
2 33 -1.658465e+04 0.000e+00 1.110e+05 2.848e-02
3 43 -1.800924e+04 0.000e+00 1.713e+05 3.925e-01
4 53 -1.849027e+04 0.000e+00 1.142e+05 1.926e-01
5 63 -1.892241e+04 0.000e+00 1.159e+05 2.183e-01
6 75 -1.897221e+04 0.000e+00 8.161e+04 4.707e-02
7 86 -1.899567e+04 0.000e+00 6.348e+04 1.504e-02
8 98 -1.902808e+04 0.000e+00 5.830e+04 2.019e-02
9 110 -1.903042e+04 0.000e+00 5.272e+04 1.861e-02
10 120 -1.903534e+04 0.000e+00 3.632e+04 5.704e-02
11 132 -1.903764e+04 0.000e+00 2.600e+04 3.853e-02
12 144 -1.903929e+04 0.000e+00 3.119e+04 2.635e-02
13 155 -1.904446e+04 0.000e+00 2.401e+04 6.100e-02
14 165 -1.904913e+04 0.000e+00 1.984e+04 1.415e-01
15 180 -1.904976e+04 0.000e+00 6.076e+02 2.586e-02
16 198 -1.904990e+04 0.000e+00 4.114e+02 6.005e-03
17 209 -1.904990e+04 0.000e+00 1.778e+03 4.275e-03
18 222 -1.904992e+04 0.000e+00 2.602e+02 2.549e-03
19 234 -1.904993e+04 0.000e+00 2.041e+02 4.194e-03
20 246 -1.904993e+04 0.000e+00 3.829e+02 2.355e-03
21 259 -1.904993e+04 0.000e+00 3.932e+01 3.069e-03
22 271 -1.904993e+04 0.000e+00 3.171e+02 8.369e-04
23 280 -1.904994e+04 0.000e+00 2.152e+01 1.910e-03
24 289 -1.904994e+04 0.000e+00 1.557e+00 8.685e-05
25 298 -1.904994e+04 0.000e+00 4.872e+00 6.453e-04
26 307 -1.904994e+04 0.000e+00 2.697e-01 1.357e-05
27 316 -1.904994e+04 0.000e+00 2.000e-02 1.297e-06
28 325 -1.904994e+04 0.000e+00 2.252e+00 9.282e-05
29 334 -1.904994e+04 0.000e+00 3.705e-01 3.815e-05
30 343 -1.904994e+04 0.000e+00 5.633e-01 3.257e-05
First-order Norm of
Iter F-count f(x) Feasibility optimality step
31 352 -1.904994e+04 0.000e+00 8.487e-02 7.262e-06
32 361 -1.904994e+04 0.000e+00 1.597e-02 2.878e-06
33 370 -1.904994e+04 0.000e+00 1.376e-02 1.335e-06
<a href = "matlab: helpview('optim','local_min_poss_with_constr','CSHelpWindow');">Local minimum possible. Constraints satisfied</a>.
fmincon stopped because the <a href = "matlab: helpview('optim','norm_curr_step_simple_fminconip','CSHelpWindow');">size of the current step</a> is less than
the value of the <a href = "matlab: helpview('optim','step_size_tol','CSHelpWindow');">step size tolerance</a> and constraints are
satisfied to within the value of the <a href = "matlab: helpview('optim','constraint_tolerance','CSHelpWindow');">constraint tolerance</a>.
<<a href = "matlab: createExitMsg({'optimlib:sqpLineSearch:Exit2basic','fmincon'},{'optimlib:sqpLineSearch:Exit2detailed','1.000000e-06','0.000000e+00','1.000000e-06'},true,true);;">stopping criteria details</a>>
Final value of minus the log posterior (or likelihood):-19049.937416
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alp 0.3560 0.4405 0.0155 beta 0.0200
bet 0.9930 0.9931 0.0019 beta 0.0020
gam 0.0085 0.0033 0.0004 norm 0.0030
mst 1.0002 1.0112 0.0003 norm 0.0070
rho 0.1290 0.6385 0.0154 beta 0.1000
psi 0.6500 0.7159 0.0447 beta 0.0500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_a 0.0354 0.0196 0.0007 invg Inf
e_m 0.0089 0.0055 0.0001 invg Inf
Log data density [Laplace approximation] is 19005.577415.
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 simple_example/metropolis\simple_example_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: 3000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 78.1333%
Chain 2: 78.7333%
Estimation::mcmc: Total number of MH draws per chain: 3000.
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 1501.
Estimation::mcmc: Finally I keep 1500 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_e_a 42.163 84.742
SE_e_m 14.585 233.349
alp 40.201 128.100
bet 16.133 11.423
gam 132.010 144.218
mst 97.178 42.566
rho 102.260 140.840
psi 44.256 59.646
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!
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 19004.438054.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alp 0.356 0.4395 0.4149 0.4664 beta 0.0200
bet 0.993 0.9929 0.9903 0.9956 beta 0.0020
gam 0.009 0.0033 0.0027 0.0040 norm 0.0030
mst 1.000 1.0113 1.0107 1.0117 norm 0.0070
rho 0.129 0.6378 0.6113 0.6624 beta 0.1000
psi 0.650 0.7122 0.6584 0.7808 beta 0.0500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_a 0.035 0.0197 0.0187 0.0208 invg Inf
e_m 0.009 0.0056 0.0054 0.0058 invg Inf
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15913.4452
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 9 -1.591345e+04 0.000e+00 3.472e+05
1 23 -1.656258e+04 0.000e+00 2.033e+05 2.318e-02
2 33 -1.662340e+04 0.000e+00 1.106e+05 3.014e-02
3 43 -1.784875e+04 0.000e+00 1.669e+05 3.889e-01
4 53 -1.840978e+04 0.000e+00 3.996e+04 1.921e-01
5 62 -1.885338e+04 0.000e+00 9.984e+04 2.493e-01
6 72 -1.885668e+04 0.000e+00 9.459e+04 1.801e-01
7 83 -1.891085e+04 0.000e+00 7.746e+04 1.870e-01
8 93 -1.896682e+04 0.000e+00 6.045e+04 7.802e-02
9 110 -1.897208e+04 0.000e+00 1.330e+04 1.713e-03
10 120 -1.897419e+04 0.000e+00 1.898e+04 1.469e-02
11 132 -1.897657e+04 0.000e+00 8.152e+03 5.876e-02
12 142 -1.898788e+04 0.000e+00 6.706e+03 2.015e-01
13 154 -1.899370e+04 0.000e+00 4.557e+03 3.499e-02
14 164 -1.899746e+04 0.000e+00 1.476e+04 4.004e-02
15 174 -1.899778e+04 0.000e+00 4.728e+03 1.705e-02
16 185 -1.899889e+04 0.000e+00 5.063e+03 2.116e-02
17 195 -1.899921e+04 0.000e+00 4.032e+03 1.887e-02
18 206 -1.899934e+04 0.000e+00 1.741e+03 9.846e-03
19 218 -1.899945e+04 0.000e+00 1.340e+03 4.471e-03
20 230 -1.899945e+04 0.000e+00 2.235e+03 3.398e-03
21 240 -1.899949e+04 0.000e+00 1.711e+02 3.437e-03
22 249 -1.899950e+04 0.000e+00 5.358e+01 1.723e-03
23 258 -1.899950e+04 0.000e+00 1.083e+00 1.814e-04
24 267 -1.899950e+04 0.000e+00 1.754e+00 7.361e-04
25 276 -1.899950e+04 0.000e+00 1.865e-01 6.955e-05
26 285 -1.899950e+04 0.000e+00 8.153e-02 7.836e-06
27 294 -1.899950e+04 0.000e+00 4.057e-01 1.398e-04
28 303 -1.899950e+04 0.000e+00 1.062e-01 3.076e-06
<a href = "matlab: helpview('optim','feasible_better_objective','CSHelpWindow');">Feasible point with lower objective function value found</a>.
<a href = "matlab: helpview('optim','local_min_poss_with_constr','CSHelpWindow');">Local minimum possible. Constraints satisfied</a>.
fmincon stopped because the <a href = "matlab: helpview('optim','norm_curr_step_simple_fminconip','CSHelpWindow');">size of the current step</a> is less than
the value of the <a href = "matlab: helpview('optim','step_size_tol','CSHelpWindow');">step size tolerance</a> and constraints are
satisfied to within the value of the <a href = "matlab: helpview('optim','constraint_tolerance','CSHelpWindow');">constraint tolerance</a>.
<<a href = "matlab: createExitMsg({'optimlib:sqpLineSearch:Exit2basic','fmincon'},{'optimlib:sqpLineSearch:Exit2detailed','1.000000e-06','0.000000e+00','1.000000e-06'},true,true);;">stopping criteria details</a>>
Final value of minus the log posterior (or likelihood):-18999.496094
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alp 0.3560 0.4524 0.0153 beta 0.0200
bet 0.9930 0.9936 0.0018 beta 0.0020
gam 0.0085 0.0033 0.0004 norm 0.0030
mst 1.0002 1.0112 0.0003 norm 0.0070
rho 0.1290 0.6222 0.0157 beta 0.1000
psi 0.6500 0.6924 0.0471 beta 0.0500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_a 0.0354 0.0206 0.0007 invg Inf
e_m 0.0089 0.0057 0.0001 invg Inf
Log data density [Laplace approximation] is 18955.250283.
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 simple_example/metropolis\simple_example_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: 3000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 78.4667%
Chain 2: 78.9333%
Estimation::mcmc: Total number of MH draws per chain: 3000.
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 1501.
Estimation::mcmc: Finally I keep 1500 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_e_a 40.540 73.452
SE_e_m 36.159 213.541
alp 21.105 109.551
bet 64.405 17.527
gam 83.941 144.636
mst 124.455 45.304
rho 165.049 155.691
psi 51.307 63.542
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!
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 18954.186167.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alp 0.356 0.4537 0.4265 0.4797 beta 0.0200
bet 0.993 0.9933 0.9910 0.9957 beta 0.0020
gam 0.009 0.0034 0.0027 0.0040 norm 0.0030
mst 1.000 1.0113 1.0108 1.0118 norm 0.0070
rho 0.129 0.6256 0.5976 0.6502 beta 0.1000
psi 0.650 0.6918 0.6256 0.7545 beta 0.0500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_a 0.035 0.0208 0.0197 0.0219 invg Inf
e_m 0.009 0.0058 0.0056 0.0060 invg Inf
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15922.3309
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 9 -1.592233e+04 0.000e+00 3.555e+05
1 23 -1.657877e+04 0.000e+00 2.019e+05 2.319e-02
2 33 -1.665671e+04 0.000e+00 1.102e+05 3.165e-02
3 43 -1.776623e+04 0.000e+00 1.615e+05 3.867e-01
4 53 -1.831273e+04 0.000e+00 7.631e+04 1.917e-01
5 62 -1.872587e+04 0.000e+00 2.255e+04 1.427e-01
6 73 -1.876725e+04 0.000e+00 8.844e+04 1.628e-01
7 83 -1.879124e+04 0.000e+00 2.439e+05 2.213e-01
8 94 -1.884378e+04 0.000e+00 9.171e+04 5.363e-02
9 103 -1.890104e+04 0.000e+00 7.311e+04 4.063e-02
10 115 -1.891610e+04 0.000e+00 6.001e+04 1.844e-02
11 127 -1.892868e+04 0.000e+00 5.126e+04 2.066e-02
12 145 -1.893185e+04 0.000e+00 3.460e+03 1.091e-03
13 155 -1.893263e+04 0.000e+00 3.287e+03 4.419e-03
14 167 -1.893382e+04 0.000e+00 1.936e+04 5.460e-02
15 177 -1.893821e+04 0.000e+00 3.385e+03 1.872e-01
16 197 -1.893901e+04 0.000e+00 3.327e+03 5.776e-04
17 207 -1.893928e+04 0.000e+00 3.217e+03 3.232e-03
18 216 -1.894281e+04 0.000e+00 2.005e+03 5.010e-02
19 226 -1.894366e+04 0.000e+00 1.678e+03 3.895e-02
20 236 -1.894377e+04 0.000e+00 2.192e+03 2.243e-02
21 247 -1.894424e+04 0.000e+00 2.186e+03 2.565e-02
22 256 -1.894446e+04 0.000e+00 1.691e+03 1.515e-02
23 265 -1.894480e+04 0.000e+00 1.208e+03 1.761e-02
24 274 -1.894483e+04 0.000e+00 8.239e+01 5.229e-03
25 283 -1.894483e+04 0.000e+00 9.845e+01 7.310e-04
26 292 -1.894483e+04 0.000e+00 5.210e+01 1.265e-04
27 301 -1.894483e+04 0.000e+00 1.285e+00 7.239e-05
28 310 -1.894483e+04 0.000e+00 7.753e+00 7.393e-04
29 319 -1.894483e+04 0.000e+00 3.823e+00 5.285e-05
30 328 -1.894483e+04 0.000e+00 6.607e-01 1.693e-05
First-order Norm of
Iter F-count f(x) Feasibility optimality step
31 337 -1.894483e+04 0.000e+00 6.861e-02 5.865e-06
32 346 -1.894483e+04 0.000e+00 2.280e+00 1.747e-04
33 355 -1.894483e+04 0.000e+00 1.040e-01 1.762e-05
34 364 -1.894483e+04 0.000e+00 4.000e-03 1.586e-06
35 373 -1.894483e+04 0.000e+00 1.989e-01 1.655e-05
36 382 -1.894483e+04 0.000e+00 3.895e-02 9.349e-06
37 391 -1.894483e+04 0.000e+00 3.041e-02 5.149e-06
38 400 -1.894483e+04 0.000e+00 8.434e-03 9.561e-07
<a href = "matlab: helpview('optim','feasible_better_objective','CSHelpWindow');">Feasible point with lower objective function value found</a>.
<a href = "matlab: helpview('optim','local_min_poss_with_constr','CSHelpWindow');">Local minimum possible. Constraints satisfied</a>.
fmincon stopped because the <a href = "matlab: helpview('optim','norm_curr_step_simple_fminconip','CSHelpWindow');">size of the current step</a> is less than
the value of the <a href = "matlab: helpview('optim','step_size_tol','CSHelpWindow');">step size tolerance</a> and constraints are
satisfied to within the value of the <a href = "matlab: helpview('optim','constraint_tolerance','CSHelpWindow');">constraint tolerance</a>.
<<a href = "matlab: createExitMsg({'optimlib:sqpLineSearch:Exit2basic','fmincon'},{'optimlib:sqpLineSearch:Exit2detailed','1.000000e-06','0.000000e+00','1.000000e-06'},true,true);;">stopping criteria details</a>>
Final value of minus the log posterior (or likelihood):-18944.833544
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alp 0.3560 0.4594 0.0152 beta 0.0200
bet 0.9930 0.9939 0.0017 beta 0.0020
gam 0.0085 0.0034 0.0004 norm 0.0030
mst 1.0002 1.0112 0.0003 norm 0.0070
rho 0.1290 0.6062 0.0161 beta 0.1000
psi 0.6500 0.6620 0.0499 beta 0.0500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_a 0.0354 0.0214 0.0007 invg Inf
e_m 0.0089 0.0059 0.0001 invg Inf
Log data density [Laplace approximation] is 18900.703593.
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 simple_example/metropolis\simple_example_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: 3000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 76.9333%
Chain 2: 78.2333%
Estimation::mcmc: Total number of MH draws per chain: 3000.
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 1501.
Estimation::mcmc: Finally I keep 1500 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_e_a 49.943 28.161
SE_e_m 60.071 149.348
alp 28.779 10.654
bet 71.184 31.914
gam 122.202 176.455
mst 12.809 136.568
rho 48.182 21.141
psi 35.237 59.326
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!
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 18899.719507.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alp 0.356 0.4632 0.4440 0.4835 beta 0.0200
bet 0.993 0.9938 0.9913 0.9963 beta 0.0020
gam 0.009 0.0034 0.0027 0.0041 norm 0.0030
mst 1.000 1.0111 1.0107 1.0116 norm 0.0070
rho 0.129 0.6099 0.5885 0.6376 beta 0.1000
psi 0.650 0.6656 0.5981 0.7407 beta 0.0500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_a 0.035 0.0217 0.0206 0.0226 invg Inf
e_m 0.009 0.0059 0.0058 0.0061 invg Inf
You did not declare endogenous variables after the estimation/calib_smoother command.
Initial value of the log posterior (or likelihood): 15926.9424
First-order Norm of
Iter F-count f(x) Feasibility optimality step
0 9 -1.592694e+04 0.000e+00 3.637e+05
1 23 -1.659083e+04 0.000e+00 2.006e+05 2.319e-02
2 33 -1.668548e+04 0.000e+00 1.098e+05 3.305e-02
3 43 -1.771768e+04 0.000e+00 1.565e+05 3.851e-01
4 53 -1.823424e+04 0.000e+00 8.693e+04 1.914e-01
5 62 -1.857093e+04 0.000e+00 3.913e+04 1.165e-01
6 73 -1.864286e+04 0.000e+00 4.999e+04 1.660e-01
7 83 -1.870581e+04 0.000e+00 2.327e+05 2.193e-01
8 94 -1.876759e+04 0.000e+00 7.647e+04 4.322e-02
9 103 -1.883383e+04 0.000e+00 7.285e+04 1.285e-01
10 115 -1.884913e+04 0.000e+00 5.853e+04 2.624e-02
11 126 -1.886089e+04 0.000e+00 3.564e+04 4.939e-02
12 137 -1.886216e+04 0.000e+00 4.341e+04 7.229e-02
13 148 -1.886600e+04 0.000e+00 5.085e+04 3.838e-02
14 158 -1.886958e+04 0.000e+00 2.394e+04 5.633e-02
15 169 -1.887086e+04 0.000e+00 2.608e+04 1.903e-02
16 181 -1.887621e+04 0.000e+00 3.449e+04 6.513e-02
17 190 -1.888601e+04 0.000e+00 8.796e+03 1.265e-01
18 205 -1.888622e+04 0.000e+00 7.155e+02 6.194e-02
19 217 -1.888625e+04 0.000e+00 5.840e+03 1.423e-02
20 227 -1.888644e+04 0.000e+00 3.185e+03 3.977e-02
21 246 -1.888655e+04 0.000e+00 4.252e+02 2.416e-03
22 256 -1.888660e+04 0.000e+00 6.418e+02 1.423e-03
23 265 -1.888688e+04 0.000e+00 4.112e+02 1.104e-02
24 274 -1.888689e+04 0.000e+00 2.600e+02 1.803e-03
25 283 -1.888689e+04 0.000e+00 1.055e+02 4.084e-04
26 292 -1.888689e+04 0.000e+00 2.099e+01 1.021e-04
27 301 -1.888689e+04 0.000e+00 1.819e+01 5.644e-05
28 310 -1.888689e+04 0.000e+00 7.515e+00 4.120e-05
29 319 -1.888689e+04 0.000e+00 1.358e+00 1.402e-05
30 328 -1.888690e+04 0.000e+00 6.204e+00 7.411e-04
First-order Norm of
Iter F-count f(x) Feasibility optimality step
31 337 -1.888690e+04 0.000e+00 1.086e+00 4.466e-05
32 346 -1.888690e+04 0.000e+00 8.891e-02 3.295e-06
33 355 -1.888690e+04 0.000e+00 1.035e-01 6.076e-06
34 364 -1.888690e+04 0.000e+00 2.000e-02 2.745e-06
35 373 -1.888690e+04 0.000e+00 3.841e-01 1.195e-04
36 382 -1.888690e+04 0.000e+00 1.078e-01 3.782e-05
37 391 -1.888690e+04 0.000e+00 1.181e-01 7.570e-05
38 400 -1.888690e+04 0.000e+00 1.750e-02 1.926e-05
39 409 -1.888690e+04 0.000e+00 5.049e-03 5.017e-06
40 418 -1.888690e+04 0.000e+00 4.000e-03 3.214e-06
41 427 -1.888690e+04 0.000e+00 1.550e-01 1.046e-04
42 436 -1.888690e+04 0.000e+00 1.198e-01 7.296e-05