i modefied the linear system for bayesian estimation,the result can estimate the parameters,but also shows erro message as follows,could someone help me with this problem? ans is it necessary to write variables’ steady state value when i use linear system for bayesian estimation?(the mod file and data.m are attached below,it will take about one hours for estimation)
---------------------------------------------------------matlab result-------------------------------------------------------------------------------
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dynare exrbyes
The element type “name” must be terminated by the matching end-tag “”.
Could not parse the file: c:\matlab7\toolbox\ccslink\ccslink\info.xml
Configuring Dynare …
[mex] Generalized QZ.
[mex] Sylvester equation solution.
[mex] Kronecker products.
[mex] Sparse kronecker products.
[mex] Bytecode evaluation.
[mex] k-order perturbation solver.
[mex] k-order solution simulation.
Starting Dynare (version 4.1.2).
Starting preprocessing of the model file …
Found 15 equation(s).
Evaluating expressions…done
Computing static model derivatives:
- order 1
Computing dynamic model derivatives: - order 1
- order 2
Processing outputs …done
Preprocessing completed.
Starting MATLAB/Octave computing.
Loading 59 observations from data.m
Initial value of the log posterior (or likelihood): -86478379.1768
==========================================================
Change in the covariance matrix = 9.9996.
Mode improvement = 86477751.3739
New value of jscale = 0.0045837
Change in the covariance matrix = 0.12271.
Mode improvement = 41.0807
RESULTS FROM POSTERIOR MAXIMIZATION
parameters
prior mean mode s.d. t-stat prior pstdev
bpai 1.500 1.2764 0.0550 23.1874 gamm 0.5000
by 0.500 1.3809 0.0775 17.8245 gamm 0.2000
rho 0.800 0.7671 0.0205 37.4001 beta 0.2000
rhoystar 0.800 1.0000 0.0037 272.8955 beta 0.2000
rhopaistar 0.800 0.7514 0.0223 33.6287 beta 0.2000
rhoistar 0.800 0.8555 0.0254 33.6212 beta 0.2000
bystar 0.500 0.6057 0.0303 19.9833 gamm 0.2000
bpaistar 1.500 2.0717 0.1067 19.4246 gamm 0.5000
taopai 0.800 0.9950 0.0046 216.0475 beta 0.2000
taoy 0.800 0.8524 0.0263 32.4419 beta 0.2000
taoi 0.800 0.6443 0.0305 21.1405 beta 0.2000
gammam 0.500 0.2795 0.0310 9.0288 gamm 0.2000
a1 0.080 0.1126 0.0024 47.7652 gamm 0.0200
a2 0.010 0.0113 0.0003 33.8159 gamm 0.0050
bystar 0.400 0.0442 0.0135 3.2781 gamm 0.2000
bpaistar 0.100 0.0065 0.0034 1.9031 gamm 0.2000
standard deviation of shocks
prior mean mode s.d. t-stat prior pstdev
uystar 0.010 5.9792 0.3683 16.2324 invg Inf
uistar 0.010 0.4683 0.0437 10.7223 invg Inf
upaistar 0.010 0.7807 0.0641 12.1738 invg Inf
upai 0.010 0.0479 0.0142 3.3642 invg Inf
uy 0.010 1.6634 0.3095 5.3748 invg Inf
ui 0.010 0.5719 0.0599 9.5458 invg Inf
Log data density [Laplace approximation] is -664.977551.
MH: Multiple chains mode.
MH: Old _mh files successfully erased!
MH: Old metropolis.log file successfully erased!
MH: Creation of a new metropolis.log file.
MH: Searching for initial values…
MH: Initial values found!
MH: Old mh_history file successfully erased!
MH: Number of mh files : 4 per block.
MH: Total number of generated files : 8.
MH: Total number of iterations : 20000.
MH: average acceptation rate per chain :
0.4213 0.2649
MH: Total number of Mh draws: 20000.
MH: Total number of generated Mh files: 4.
MH: I’ll use mh-files 2 to 4.
MH: In mh-file number 2 i’ll start at line 4791.
MH: Finally I keep 10000 draws.
MH: I’m computing the posterior mean and covariance… Done!
MH: I’m computing the posterior log marginale density (modified harmonic mean)…
MH: Modified harmonic mean estimator, done!
ESTIMATION RESULTS
Log data density is -672.960240.
parameters
prior mean post. mean conf. interval prior pstdev
bpai 1.500 1.2924 1.2312 1.3669 gamm 0.5000
by 0.500 1.4766 1.3966 1.5716 gamm 0.2000
rho 0.800 0.7903 0.7604 0.8180 beta 0.2000
rhoystar 0.800 0.9987 0.9962 1.0000 beta 0.2000
rhopaistar 0.800 0.7360 0.7007 0.7639 beta 0.2000
rhoistar 0.800 0.8891 0.8549 0.9232 beta 0.2000
bystar 0.500 0.5532 0.4733 0.6599 gamm 0.2000
bpaistar 1.500 1.9231 1.7223 2.1458 gamm 0.5000
taopai 0.800 0.9960 0.9933 0.9997 beta 0.2000
taoy 0.800 0.8403 0.8019 0.8801 beta 0.2000
taoi 0.800 0.6262 0.5599 0.6887 beta 0.2000
gammam 0.500 0.2758 0.2293 0.3459 gamm 0.2000
a1 0.080 0.1110 0.1037 0.1178 gamm 0.0200
a2 0.010 0.0112 0.0108 0.0116 gamm 0.0050
bystar 0.400 0.0586 0.0280 0.0853 gamm 0.2000
bpaistar 0.100 0.0091 0.0046 0.0139 gamm 0.2000
standard deviation of shocks
prior mean post. mean conf. interval prior pstdev
uystar 0.010 5.1583 4.1687 6.1078 invg Inf
uistar 0.010 0.4750 0.4207 0.5454 invg Inf
upaistar 0.010 0.8622 0.7433 0.9425 invg Inf
upai 0.010 0.0446 0.0340 0.0550 invg Inf
uy 0.010 1.7347 1.2911 2.2272 invg Inf
ui 0.010 0.5445 0.4514 0.6587 invg Inf
??? Error using ==> eval
Subscripted assignment dimension mismatch.
Error in ==> PlotPosteriorDistributions at 132
eval(‘oo_.prior_density.parameters.’ name ‘(:,1) = x2;’])
Error in ==> dynare_estimation_1 at 1075
oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
Error in ==> dynare_estimation at 62
dynare_estimation_1(var_list,varargin{:});
Error in ==> exrbyes at 255
dynare_estimation(var_list_);
Error in ==> dynare at 132
evalin(‘base’,fname) ;
data.m (3.79 KB)
exrbyes.mod (2.67 KB)