Dear all:
when I use conditional_variance_decomposition=[1 5 10] in estimation command I want to get the percentage contribution of each shock to the endogenous variable in period 1,5,10. I can see the picture after the estimation. But where can I find the number of percentage contribution of each shock ?
Thank you all !
See the manual
Good morning everyone, I got a confusing result as below in my model. So don’t know how to go about it. Thanks for your usual response.
Posterior mean variance decomposition (in percent)
eps_a eps_rev eps_d eps_g eps_ff eps_fy eps_b eps_r eps_y eps_df eps_t eps_rn
y NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
g NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
rev 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
d NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
r NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Without the full output or the codes it is impossible to tell what is going on. Are these variables stationary? Or do they have a unit root?
Thank You Professor Jpfeifer. Here is my code and the error message.
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)…
Estimation::marginal density: The support of the weighting density function is not large enough…
Estimation::marginal density: I increase the variance of this distribution.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: Let me try again.
Estimation::marginal density: There’s probably a problem with the modified harmonic mean estimator.
ESTIMATION RESULTS
Log data density is -Inf.
parameters
prior mean post. mean 90% HPD interval prior pstdev
h 0.900 0.8036 0.8027 0.8048 beta 0.1000
sigma 3.000 2.9816 2.9814 2.9819 norm 1.0000
pii 0.250 0.2742 0.2738 0.2748 norm 0.1000
rho 0.350 0.3435 0.3433 0.3438 beta 0.1000
epsilon 0.990 0.9945 0.9944 0.9947 norm 0.1000
phi 11.380 11.3494 11.3493 11.3495 norm 1.0000
alpha 0.350 0.2972 0.2969 0.2974 beta 0.1000
delta 0.400 0.3664 0.3663 0.3665 beta 0.1000
nu 0.650 0.6260 0.6255 0.6265 beta 0.1000
upsilon 0.500 0.4486 0.4481 0.4492 norm 0.1000
zeta 0.650 0.6596 0.6595 0.6597 beta 0.1000
psi 0.700 0.9960 0.9960 0.9960 beta 0.1000
kappa 0.600 0.7051 0.7046 0.7058 gamm 0.1000
omega 0.500 0.5024 0.5020 0.5026 beta 0.1000
estar 0.700 0.6884 0.6882 0.6887 beta 0.1000
kappa_alpha 0.700 0.7324 0.7318 0.7330 beta 0.1000
upsilon_estar 0.600 0.6544 0.6541 0.6547 beta 0.1000
upsilon_y 0.650 0.6544 0.6539 0.6549 beta 0.1000
upsilon_exr 0.400 0.3395 0.3386 0.3402 beta 0.1000
rho_z 0.700 0.7139 0.7138 0.7140 gamm 0.1000
rho_TP 0.700 1.0000 1.0000 1.0000 gamm 0.1000
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
eps_a 0.250 0.1559 0.1551 0.1565 invg Inf
eps_rev 0.250 0.1357 0.1353 0.1362 invg Inf
eps_d 0.250 0.0892 0.0891 0.0893 invg Inf
eps_g 0.250 0.1198 0.1196 0.1200 invg Inf
eps_ff 0.250 0.1394 0.1392 0.1395 invg Inf
eps_fy 0.250 0.1215 0.1214 0.1216 invg Inf
eps_b 0.250 0.0758 0.0758 0.0759 invg Inf
eps_r 0.250 0.1392 0.1389 0.1395 invg Inf
eps_y 0.250 0.3112 0.3111 0.3114 invg Inf
eps_df 0.250 0.0911 0.0908 0.0915 invg Inf
eps_t 0.250 0.3737 0.3726 0.3746 invg Inf
eps_rn 0.250 0.0736 0.0736 0.0738 invg Inf
Estimation::mcmc: Posterior (dsge) IRFs…
Estimation::mcmc: Posterior IRFs, done!
Estimation::compute_moments_varendo: I’m computing endogenous moments (this may take a while)…
Posterior mean variance decomposition (in percent)
eps_a eps_rev eps_d eps_g eps_ff eps_fy eps_b eps_r eps_y eps_df eps_t eps_rn
y NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
g NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
rev 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
d NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
r NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Done!
Estimation::mcmc: One step ahead forecast (filtered variables)
Estimation::mcmc: One step ahead forecast (filtered variables), done!
WARNING IDENTIFICATION: Previously the diffuse_filter option was used, but it was not passed to the identification command. This may result in problems if your model contains unit roots.
Prior distribution for parameter h has unbounded density!
Error using dynare_estimation_init (line 501)
analytic derivation is incompatible with diffuse filter
Error in dynare_identification (line 297)
[dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_,
bounds] = dynare_estimation_init(M_.endo_names, fname, 1, M_, options_, oo_,
estim_params_, bayestopt_);
Error in musa.driver (line 592)
dynare_identification(options_ident);
Error in dynare (line 293)
evalin(‘base’,[fname ‘.driver’]) ;
daudaquarter.xls (33.5 KB)
musa.mod (3.8 KB)
You still did not fix the problem in