Computing dynamic model derivatives:
- order 1
- order 2
Processing outputs ...done
Preprocessing completed.
Starting MATLAB/Octave computing.
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Loading 1001 observations from simuldataRBC.m
Restricting the sample to observations 500 to 699. Using in total 200 observations.
Initial value of the log posterior (or likelihood): 668.828
==========================================================
Change in the covariance matrix = 10.
Mode improvement = 6.6106
New value of jscale = 0.0004874
==========================================================
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Change in the covariance matrix = 0.1227.
Mode improvement = 8.271
New value of jscale = 3.4406
==========================================================
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Change in the covariance matrix = 0.12732.
Mode improvement = 0.2081
New value of jscale = 0.68847
==========================================================
Optimal value of the scale parameter = 0.68847
Final value of the log posterior (or likelihood): -683.917
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
alpha 0.350 0.3366 0.0102 beta 0.0200
beta 0.990 0.9900 0.0019 beta 0.0020
delta 0.025 0.0251 0.0029 beta 0.0030
psi 1.750 1.7646 0.0973 gamm 0.1000
rho 0.950 0.9415 0.0246 beta 0.0500
epsilon 10.000 9.9580 0.5001 gamm 0.5000
standard deviation of shocks
prior mean mode s.d. prior pstdev
e 0.010 0.0097 0.0008 invg Inf
Log data density [Laplace approximation] is 658.492860.
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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 RBC_Est/metropolis\RBC_Est_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: 2000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 32.634%
Chain 2: 31.784%
warning: estimation:: MCMC convergence diagnostics are not computed because the total number of iterations is less than 2000!
Estimation::mcmc: Total number of MH draws: 2000.
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 900.
Estimation::mcmc: Finally I keep 1100 draws.
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 657.671079.
posterior_moments: There are not enough draws computes to compute HPD Intervals. Skipping their computation.
posterior_moments: There are not enough draws computes to compute deciles. Skipping their computation.
parameters
prior mean post. mean 90% HPD interval prior pstdev
alpha 0.350 0.3386 0.3205 0.3559 beta 0.0200
beta 0.990 0.9895 0.9862 0.9926 beta 0.0020
delta 0.025 0.0254 0.0210 0.0291 beta 0.0030
psi 1.750 1.7801 1.6139 1.9186 gamma 0.1000
rho 0.950 0.9461 0.9087 0.9911 beta 0.0500
epsilon 10.000 10.0741 9.3905 10.8789 gamma 0.5000
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e 0.010 0.0102 0.0090 0.0116 invg Inf
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Total computing time : 0h38m49s
Note: warning(s) encountered in MATLAB/Octave code