First, thank you to all the senior members of the forum for your continuous support and help. I have learned a lot of knowledge and experience from the forum.
Here, I would also like to ask the professors and experts to help me diagnose what problems exist in the model. The DSGE model estimation results are as follows:
dynare msDSGEPF
Starting Dynare (version 5.4).
Calling Dynare with arguments: none
Starting preprocessing of the model file …
Found 185 equation(s).
Evaluating expressions…done
Computing static model derivatives (order 1).
Computing dynamic model derivatives (order 2).
Processing outputs …
done
Preprocessing completed.
CountryName =
STEADY-STATE RESULTS:
CapCU 1
iB_obs 1
CapiB 0
lnibarB 1
CapiBf 0
CapYG_obs 1
CapYGf_obs 1
deltalnCapYG 0
deltalnCapYGf 0
CapF_obs 1
CapFf_obs 1
deltalnCapF 0
deltalnCapFf 0
C_obs 2.49416
CapC 1
lnCbar 2.49416
iG_obs 1
CapiG 0
lnibarG 1
CapN 1
UL_obs 1
CapUL 0
lnUbarL 1
CapCC 1
L_obs 1.36834
CapL 1
lnLbar 1.36834
W_obs 2.91414
CapW 1
lnWbar 2.91414
EPSW_obs 0.996506
CapEPSW 1
lnEPSWbar 0.996506
CapQ 1
Cappiw 0
CapR 1
CapMf 1
CapAT 1
CapgAT 0
CapPH 1
CapCHH 1
CapH 1
VH_obs 2.91414
CapVH 1
lnVbarH 2.91414
CapPIH 1
CapYP 1
CapA 1
lnCapvTldA 0
lnCapvAf 0
CapA_to_PYY 0
Capk 0
CapRH 0
CapIK 1
CapQK 1
CapBFF 1
CapIH 1
CapPIF 1
CapK 1
CapCFF 1
CapQH 1
CapBHH 1
VF_obs 3.98754
CapVF 1
lnVbarF 3.98754
CapBBH 1
CapBBF 1
AB_obs 4.98754
CapAB 1
lnAbarB 3.98754
CapIB 1
CapUK 1
CappiY 0
CapKB 1
CapDH 1
CapDF 1
CapDHh 1
CapDFh 1
CapDHf 1
CapDFf 1
CapDHff 1
CapDFff 1
CapABH 1
CapABF 1
CapCBH 1
CapCBF 1
CapRK 0
CapMs 1
CapdeltaB 0
CapdeltaH 0
CapdeltaF 0
CapQB 1
CapGC 1
CapGI 1
CapKG 1
CaptauK 0
CaptauL 0
CaptauC 0
Capr 0
CapiH 0
CapiHE 0
CapiF 0
CapiFE 0
CaptauF 0
CaptauH 0
CapiHf 0
CapiFf 0
CapkR 0
CappiC 0
CappiG 0
CappiI 0
PY_obs 1.59875
CapPY 1
lnPbarY 1.59875
CapPYf 1
CappiX 0
CappiM 0
CapT_to_PYY 0
CapFD_to_PYY 0
CapphiH 0
CapphiF 0
Y_obs 2.49416
CapY 1
lnYbar 2.49416
CapG 1
I 2.49416
CapI 1
lnIbar 2.49416
PC_obs 1.59875
CapPC 1
lnPbarC 1.59875
CapPI 1
CapPG 1
CapTC_to_PYY 0
CapTCD_to_PYY 0
CapTCN_to_PYY 0
CapTB_to_PYY 0
X_obs 2.49416
CapX 1
lnXbar 2.49416
CapPD_to_PYY 0
CapFXI 0
CapPX 1
CapPXf 1
CapPM 1
M_obs 2.49416
CapM 1
lnMbar 2.49416
CapiGE 0
CapdeltaHf 0
CapdeltaFf 0
CapdeltaHE 0
CapdeltaFE 0
CapCA_to_PYY 0
CapDG_to_PYY 0
lnCapvA 0
lnCapvN 0
lnCapvC 0
lnCapvI 0
lnCapvX 0
lnCapvM 0
CapvB 0
CapvBf 0
CapvTldB 0
CapvH 0
CapvS 0
CapvSf 0
CapvTldS 0
CapvEPSW 0
lnCaphA 0
lnCaphN 0
lnCaphI 0
lnCaphC 0
lnCaphX 0
lnCaphM 0
lnCaphB 0
lnCaphH 0
lnCaphS 0
lnCaphepsw 0
lnCapThetaY 0
lnCapThetaL 0
CapviB 0
CapvG 0
lnCapvP 0
Capvphi 0
MODEL_DIAGNOSTICS: No obvious problems with this mod-file were detected.
Residuals of the static equations:
Equation number 1 : 0 : Equation (1) CappiY LF
Equation number 2 : 0 : Equation (2) CapPY LF
Equation number 3 : 0 : SE Equation (2) PY_obs
Equation number 4 : 0 : Equation (3) ln(CapY) LF
Equation number 5 : 0 : SE Equation Y_obs
Equation number 6 : 0 : Equation (4) CappiC LF
Equation number 7 : 0 : Equation (5) ln(CapPC)
Equation number 8 : 0 : SE Equation (5) ln(CapPC)
Equation number 9 : 0 : Equation (6) ln(CapC)
Equation number 10 : 0 : C_obs
Equation number 11 : 0 : Equation (7) ln(CapCU) LF
Equation number 12 : 0 : Equation (8) ln(CapCC)
Equation number 13 : 0 : Equation (9) CappiI LF
Equation number 14 : 0 : Equation (10) CapPI LF
Equation number 15 : 0 : Equation (11) ln(CapI)
Equation number 16 : 0 : I
Equation number 17 : 0 : Equation (12) ln(CapIH) LF
Equation number 18 : 0 : Equation (13) ln(CapQH) LF
Equation number 19 : 0 : Equation (14) ln(CapPH)
Equation number 20 : 0 : Equation (15) CapRH LF
Equation number 21 : 0 : Equation (16) CapH
Equation number 22 : 0 : Equation (17) ln(CapIK) LF
Equation number 23 : 0 : Equation (18) CapQK LF
Equation number 24 : 0 : Equation (19) CapUK
Equation number 25 : 0 : Equation (20) CapRK LF
Equation number 26 : 0 : Equation (21) ln(CapK(+1))
Equation number 27 : 0 : Equation (22) CappiG
Equation number 28 : 0 : Equation (23) CapPG
Equation number 29 : 0 : Equation (24) ln(CapG)
Equation number 30 : 0 : Equation (25) ln(CapGC)
Equation number 31 : 0 : Equation (26) ln(CapGI)
Equation number 32 : 0 : Equation (27) ln(CapKG)
Equation number 33 : 0 : Equation (28) CappiX LF
Equation number 34 : 0 : Equation (29) CapPX
Equation number 35 : 0 : Equation (30) CapX
Equation number 36 : 0 : X_obs
Equation number 37 : 0 : Equation (31) CappiM LF
Equation number 38 : 0 : Equation (32) ln(CapPM)
Equation number 39 : 0 : Equation (33) ln(CapM)
Equation number 40 : 0 : M_obs
Equation number 41 : 0 : Equation (34) CapiB
Equation number 42 : 0 : iB_obs
Equation number 43 : 0 : Equation (35) CapiGE
Equation number 44 : 0 : Equation (36) CapiG LF
Equation number 45 : 0 : iG_obs
Equation number 46 : 0 : Equation (37) CapvBf
Equation number 47 : 0 : Equation (37) CapvTldB
Equation number 48 : 0 : Equation (39) ln(CapVH) LF
Equation number 49 : 0 : VH_obs
Equation number 50 : 0 : Equation (40) ln(CapPIH)
Equation number 51 : 0 : Equation (41) ln(CapBHH)
Equation number 52 : 0 : Equation (42) ln(CapCHH)
Equation number 53 : 0 : Equation (43) ln(CapDH(+1)) LF
Equation number 54 : 0 : Equation (44) ln(CapDHh(+1))
Equation number 55 : 0 : Equation (45) CapDHf LF
Equation number 56 : 0 : Equation (46) ln(CapVF) LF
Equation number 57 : 0 : VF_obs
Equation number 58 : 0 : Equation (47) CapvTldS
Equation number 59 : 0 : CapvTldS
Equation number 60 : 0 : Equation (48) CapPIF
Equation number 61 : 0 : Equation (49) CapBFF
Equation number 62 : 0 : Equation (50) ln(CapCFF)
Equation number 63 : 0 : Equation (51) CapDF LF
Equation number 64 : 0 : Equation (52) CapDFh
Equation number 65 : 0 : Equation (53) CapDFf LF
Equation number 66 : 0 : Equation (54) ln(CapMs(+1))
Equation number 67 : 0 : Equation (55) ln(CapAB(+1))
Equation number 68 : 0 : AB_obs
Equation number 69 : 0 : Equation (56) CapiHE
Equation number 70 : 0 : Equation (57) CapiH -CapiH(-1) LF
Equation number 71 : 0 : Equation (58) ln(CapBBH)
Equation number 72 : 0 : Equation (59) CapCBH
Equation number 73 : 0 : Equation (60) CapABH LF
Equation number 74 : 0 : Equation (61) CapiFE
Equation number 75 : 0 : Equation (62) CapiF LF
Equation number 76 : 0 : Equation (63) CapBBF
Equation number 77 : 0 : Equation (64) CapCBF
Equation number 78 : 0 : Equation (65) CapABF LF
Equation number 79 : 0 : Equation (66) ln(CapIB) LF
Equation number 80 : 0 : Equation (67) ln(CapQB) LF
Equation number 81 : 0 : Equation (68) Capk
Equation number 82 : 0 : Equation (69) CapKB
Equation number 83 : 0 : Equation (70) deltalnCapF
Equation number 84 : 0 : Equation (70) deltalnCapFf
Equation number 85 : 0 : Equation (71) ln(CapF_obs)
Equation number 86 : 0 : SE Equation (71) CapFf_obs
Equation number 87 : 0 : Equation (72) CapdeltaB
Equation number 88 : 0 : Equation 73 CapdeltaHE
Equation number 89 : 0 : Equation 74 CapdeltaH
Equation number 90 : 0 : Equation 75 CapdeltaFE
Equation number 91 : 0 : Equation 76 CapdeltaF
Equation number 92 : 0 : Equation (77) CapkR LF
Equation number 93 : 0 : Equation 78 CapphiH
Equation number 94 : 0 : Equation 79 CapphiF
Equation number 95 : 0 : Equation 80 CapEPSW LF
Equation number 96 : 0 : EPSW_obs
Equation number 97 : 0 : Equation 81 CapQ
Equation number 98 : 0 : Equation 82 CapFXI
Equation number 99 : 0 : Equation 83 Capr(+1) LF
Equation number 100 : 0 : Equation (84) CapR
…
EIGENVALUES:
Modulus Real Imaginary
7.513e-18 -7.513e-18 0
1.854e-17 1.854e-17 0
3.864e-17 -3.864e-17 0
5.453e-17 -5.453e-17 0
1.176e-16 1.176e-16 0
1.346e-16 -1.346e-16 0
1.804e-16 1.804e-16 0
3.105e-16 3.105e-16 0
3.192e-16 -3.192e-16 0
4.058e-16 -4.058e-16 0
4.911e-16 4.911e-16 0
5.012e-16 5.012e-16 0
5.541e-16 -5.541e-16 0
7.18e-16 -7.18e-16 0
8.004e-16 -8.004e-16 0
1.045e-09 1.045e-09 0
1.045e-09 -1.045e-09 0
3.53e-09 -3.53e-09 0
3.53e-09 3.53e-09 0
0.4 0.4 0
0.4 0.4 0
0.4 0.4 0
0.4 0.4 0
0.6 0.6 0
0.7166 0.7086 0.1065
0.7166 0.7086 -0.1065
0.7319 0.7142 0.1599
0.7319 0.7142 -0.1599
0.7337 0.7337 0
0.7466 0.7466 0
0.7498 0.7498 0
0.7633 0.7358 0.2031
0.7633 0.7358 -0.2031
0.7704 0.7704 0
0.7757 0.7581 0.164
0.7757 0.7581 -0.164
0.791 0.791 0
0.7999 0.7999 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8 0.8 0
0.8041 0.7771 0.2069
0.8041 0.7771 -0.2069
0.8056 0.8056 0.005036
0.8056 0.8056 -0.005036
0.8072 0.8072 0
0.8327 0.8327 0
0.8932 0.8777 0.1659
0.8932 0.8777 -0.1659
0.9217 0.9217 0
0.9315 0.9281 0.07891
0.9315 0.9281 -0.07891
0.95 0.95 0
0.952 0.952 0
0.9593 0.9593 0
0.9687 0.9687 0
0.9811 0.9811 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9828 0.9828 0
0.9912 0.9912 0
1.005 1.005 0
1.007 1.007 0
1.018 1.018 0
1.031 1.031 0
1.058 1.058 0.000458
1.058 1.058 -0.000458
1.074 1.072 0.06536
1.074 1.072 -0.06536
1.105 1.099 0.1207
1.105 1.099 -0.1207
1.169 1.169 0
1.199 1.166 0.2796
1.199 1.166 -0.2796
1.32 1.271 0.3551
1.32 1.271 -0.3551
1.323 1.322 0.0447
1.323 1.322 -0.0447
1.34 1.34 0
1.357 1.357 0
1.953 1.953 0
1.759e+17 1.759e+17 0
2.07e+17 -2.07e+17 0
2.138e+17 -2.138e+17 0
7.341e+17 7.341e+17 0
1.441e+18 -1.441e+18 0
3.487e+18 -3.487e+18 0
5.694e+18 -5.694e+18 0
6.424e+18 6.424e+18 0
7.053e+18 -7.053e+18 0
1.336e+19 -1.336e+19 0
2.706e+19 2.706e+19 0
7.066e+19 -7.066e+19 0
6.254e+20 6.254e+20 0
2.385e+21 2.385e+21 0
4.985e+21 4.985e+21 0
1.086e+22 1.086e+22 0
8.748e+29 8.748e+29 0
1.016e+33 -1.016e+33 0
1.299e+35 1.299e+35 0
Inf Inf 0
Inf Inf 0
Inf Inf 0
There are 42 eigenvalue(s) larger than 1 in modulus
for 42 forward-looking variable(s)
The rank condition is verified.
options =
number: 100
nscale: 500
nclimb: 100
target: 0.2340
maxiter: 100
tune_mh_jscale: 100
options =
number: 100
nscale: 500
nclimb: 100
target: 0.2340
maxiter: 100
tune_mh_jscale: 100
prior_mc: 100
Estimation using a non linear filter!
警告: Your prior allows for negative standard deviations for structural shocks. Due to working with variances, Dynare will be able to continue, but it is
recommended to change your prior.
In initial_estimation_checks (line 190)
In dynare_estimation_1 (line 159)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
Initial value of the log posterior (or likelihood): -7.097610367854506e+32
警告: gmhmaxlik: Unknown option (MaxIter)!
In gmhmaxlik (line 59)
In dynare_minimize_objective (line 336)
In dynare_estimation_1 (line 221)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
==========================================================
Change in the posterior covariance matrix = 1.
Change in the posterior mean = 0.
Current mode = 7.097610367854506e+32
Mode improvement = 0
New value of jscale = 0.2
==========================================================
Change in the posterior covariance matrix = 0.
Change in the posterior mean = 0.
Current mode = 7.097610367854506e+32
Mode improvement = 0
New value of jscale = 0.2
==========================================================
Change in the posterior covariance matrix = 0.
Change in the posterior mean = 0.
Current mode = 7.097610367854506e+32
Mode improvement = 0
New value of jscale = 0.2
警告: 矩阵为奇异工作精度。
In gmhmaxlik (line 117)
In dynare_minimize_objective (line 336)
In dynare_estimation_1 (line 221)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
Optimal value of the scale parameter = 0.2
Final value of minus the log posterior (or likelihood):709761036785450570000000000000000.000000
POSTERIOR KERNEL OPTIMIZATION PROBLEM!
(minus) the hessian matrix at the “mode” is not positive definite!
=> posterior variance of the estimated parameters are not positive.
You should try to change the initial values of the parameters using
the estimated_params_init block, or use another optimization routine.
The following parameters are at the prior bound: SE_CapSigmaA, SE_CapSigmaN, SE_CapSigmaC, SE_CapSigmaI, SE_CapSigmaX, SE_CapSigmaM, SE_CapSigmaB, SE_CapSigmaH, SE_CapSigmaS, SE_CapSigmaepsw, SE_CapSigmaThetaY, SE_CapSigmaThetaL, SE_CapSigmaiB, SE_CapSigmaP, SE_CapSigmaG, SE_CapSigmaphi, SE_barSigmaPY, SE_barSigmaY, SE_barSigmaPC, SE_barSigmaC, SE_barSigmaI, SE_barSigmaX, SE_barSigmaM, SE_barSigmaVH, SE_barSigmaVF, SE_barSigmaAB, SE_barSigmaEPSW, SE_barSigmaW, SE_barSigmaL, SE_barSigmaiB, SE_barSigmaiG, SE_barSigmaUL, SE_EOBS_CapPY, SE_EOBS_CapY
Some potential solutions are:
- Check your model for mistakes.
- Check whether model and data are consistent (correct observation equation).
- Shut off prior_trunc.
- Change the optimization bounds.
- Use a different mode_compute like 6 or 9.
- Check whether the parameters estimated are identified.
- Check prior shape (e.g. Inf density at bound(s)).
- Increase the informativeness of the prior.
警告: The results below are most likely wrong!
In dynare_estimation_1 (line 318)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
警告: 矩阵为奇异值、接近奇异值或缩放错误。结果可能不准确。RCOND = NaN。
In dynare_estimation_1 (line 341)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
RESULTS FROM POSTERIOR ESTIMATION
standard deviation of shocks
prior mean mode s.d. prior pstdev
CapSigmaA 1.0000 1.0000 NaN norm 0.1000
CapSigmaN 1.0000 1.0000 NaN norm 0.1000
CapSigmaC 1.0000 1.0000 NaN norm 0.1000
CapSigmaI 1.0000 1.0000 NaN norm 0.1000
CapSigmaX 1.0000 1.0000 NaN norm 0.1000
CapSigmaM 1.0000 1.0000 NaN norm 0.1000
CapSigmaB 1.0000 1.0000 NaN norm 0.0250
CapSigmaH 1.0000 1.0000 NaN norm 0.0500
CapSigmaS 1.0000 1.0000 NaN norm 0.1000
CapSigmaepsw 1.0000 1.0000 NaN norm 0.1000
CapSigmaThetaY 1.0000 1.0000 NaN norm 1.0000
CapSigmaThetaL 1.0000 1.0000 NaN norm 1.0000
CapSigmaiB 1.0000 1.0000 NaN norm 0.0250
CapSigmaP 1.0000 1.0000 NaN norm 0.0500
CapSigmaG 1.0000 1.0000 NaN norm 0.1000
CapSigmaphi 1.0000 1.0000 NaN norm 0.1000
barSigmaPY 0.1000 1.0000 NaN norm 0.0500
barSigmaY 0.1000 1.0000 NaN norm 0.0500
barSigmaPC 0.1000 1.0000 NaN norm 0.0500
barSigmaC 0.1000 1.0000 NaN norm 0.0500
barSigmaI 0.1000 1.0000 NaN norm 0.0500
barSigmaX 0.1000 1.0000 NaN norm 0.0500
barSigmaM 0.1000 1.0000 NaN norm 0.0500
barSigmaVH 0.1000 1.0000 NaN norm 0.0500
barSigmaVF 0.1000 1.0000 NaN norm 0.0500
barSigmaAB 0.1000 1.0000 NaN norm 0.0500
barSigmaEPSW 0.1000 1.0000 NaN norm 0.0500
barSigmaW 0.1000 1.0000 NaN norm 0.0500
barSigmaL 0.1000 1.0000 NaN norm 0.0500
barSigmaiB 0.1000 1.0000 NaN norm 0.0500
barSigmaiG 0.1000 1.0000 NaN norm 0.0500
barSigmaUL 0.1000 1.0000 NaN norm 0.0500
standard deviation of measurement errors
prior mean mode s.d. prior pstdev
CapPY 0.0505 0.0010 NaN unif 0.0286
CapY 0.0505 0.0010 NaN unif 0.0286
Log data density [Laplace approximation] is NaN.
mh_jscale has been set equal to 0
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: I couldn’t get a valid initial value in 100 trials.
Estimation::mcmc: You should reduce mh_init_scale…
Estimation::mcmc: Parameter mh_init_scale is equal to 0.400000.
Estimation::mcmc: Enter a new value… 0.2
Estimation::mcmc: I couldn’t get a valid initial value in 100 trials.
Estimation::mcmc: You should reduce mh_init_scale…
Estimation::mcmc: Parameter mh_init_scale is equal to 0.200000.
Estimation::mcmc: Enter a new value… 0.1
Estimation::mcmc: I couldn’t get a valid initial value in 100 trials.
Estimation::mcmc: You should reduce mh_init_scale…
Estimation::mcmc: Parameter mh_init_scale is equal to 0.100000.
Estimation::mcmc: Enter a new value… 0.05
Estimation::mcmc: I couldn’t get a valid initial value in 100 trials.
Estimation::mcmc: You should reduce mh_init_scale…
Estimation::mcmc: Parameter mh_init_scale is equal to 0.050000.
Estimation::mcmc: Enter a new value… 0.01
Estimation::mcmc: Initial values found!
Estimation::mcmc: Write details about the MCMC… Ok!
Estimation::mcmc: Details about the MCMC are available in msDSGEPF/metropolis\msDSGEPF_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: 1250.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 0%
Chain 2: 0%
Estimation::mcmc: Total number of MH draws per chain: 1250.
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 626.
Estimation::mcmc: Finally I keep 625 draws per chain.
MCMC Inefficiency factors per block
Parameter Block 1 Block 2
SE_CapSigmaA 207.117 207.117
SE_CapSigmaN 207.117 207.117
SE_CapSigmaC 207.117 207.117
SE_CapSigmaI 207.117 207.117
SE_CapSigmaX 207.117 207.117
SE_CapSigmaM 207.117 207.117
SE_CapSigmaB 207.117 207.117
SE_CapSigmaH 207.117 207.117
SE_CapSigmaS 207.117 207.117
SE_CapSigmaepsw 207.117 207.117
SE_CapSigmaThetaY 207.117 207.117
SE_CapSigmaThetaL 207.117 207.117
SE_CapSigmaiB 207.117 207.117
SE_CapSigmaP 207.117 207.117
SE_CapSigmaG 207.117 207.117
SE_CapSigmaphi 207.117 207.117
SE_barSigmaPY 207.117 207.117
SE_barSigmaY 207.117 207.117
SE_barSigmaPC NaN 207.117
SE_barSigmaC 207.117 207.117
SE_barSigmaI 207.117 207.117
SE_barSigmaX 207.117 207.117
SE_barSigmaM 207.117 207.117
SE_barSigmaVH 207.117 207.117
SE_barSigmaVF 207.117 207.117
SE_barSigmaAB 207.117 207.117
SE_barSigmaEPSW 207.117 207.117
SE_barSigmaW 207.117 207.117
SE_barSigmaL 207.117 207.117
SE_barSigmaiB 207.117 207.117
SE_barSigmaiG 207.117 207.117
SE_barSigmaUL 207.117 207.117
SE_EOBS_CapPY 207.117 207.117
SE_EOBS_CapY 207.117 207.117
警告: estimation:: MCMC convergence diagnostics are not computed because the total number of iterations is not bigger than 2000!
In McMCDiagnostics (line 127)
In dynare_estimation_1 (line 500)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
Estimation::marginal density: I’m computing the posterior mean and covariance… 警告: 矩阵接近奇异值，或者缩放错误。结果可能不准确。RCOND = 3.274059e-19。
In compute_mh_covariance_matrix (line 77)
In marginal_density
In dynare_estimation_1 (line 509)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
警告: 矩阵接近奇异值，或者缩放错误。结果可能不准确。RCOND = 3.274059e-19。
In marginal_density (line 59)
In dynare_estimation_1 (line 509)
In dynare_estimation (line 118)
In msDSGEPF.driver (line 2067)
In dynare (line 278)
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.
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
CapSigmaA 1.000 1.0590 1.0003 1.1177 norm 0.1000
CapSigmaN 1.000 0.8454 0.6889 1.0019 norm 0.1000
CapSigmaC 1.000 0.7888 0.5740 1.0035 norm 0.1000
CapSigmaI 1.000 0.6432 0.2926 0.9937 norm 0.1000
CapSigmaX 1.000 0.9209 0.8308 1.0109 norm 0.1000
CapSigmaM 1.000 0.8011 0.5788 1.0235 norm 0.1000
CapSigmaB 1.000 1.1171 0.9751 1.2591 norm 0.0250
CapSigmaH 1.000 0.9375 0.8729 1.0020 norm 0.0500
CapSigmaS 1.000 1.3585 1.0095 1.7076 norm 0.1000
CapSigmaepsw 1.000 1.2981 0.9920 1.6042 norm 0.1000
CapSigmaThetaY 1.000 1.0333 1.0009 1.0656 norm 1.0000
CapSigmaThetaL 1.000 0.9443 0.8869 1.0017 norm 1.0000
CapSigmaiB 1.000 1.2274 0.9940 1.4609 norm 0.0250
CapSigmaP 1.000 0.7742 0.5414 1.0071 norm 0.0500
CapSigmaG 1.000 1.1408 1.0121 1.2695 norm 0.1000
CapSigmaphi 1.000 0.8673 0.7324 1.0022 norm 0.1000
barSigmaPY 0.100 0.9218 0.8399 1.0037 norm 0.0500
barSigmaY 0.100 0.8629 0.7313 0.9945 norm 0.0500
barSigmaPC 0.100 1.1207 1.0112 1.2303 norm 0.0500
barSigmaC 0.100 0.8439 0.6888 0.9991 norm 0.0500
barSigmaI 0.100 0.7895 0.5746 1.0044 norm 0.0500
barSigmaX 0.100 1.1165 1.0117 1.2212 norm 0.0500
barSigmaM 0.100 0.9138 0.8306 0.9970 norm 0.0500
barSigmaVH 0.100 1.0713 0.9981 1.1446 norm 0.0500
barSigmaVF 0.100 0.9301 0.8592 1.0009 norm 0.0500
barSigmaAB 0.100 1.0528 0.9978 1.1078 norm 0.0500
barSigmaEPSW 0.100 0.4808 -0.0258 0.9873 norm 0.0500
barSigmaW 0.100 1.0956 1.0048 1.1863 norm 0.0500
barSigmaL 0.100 1.3635 0.9856 1.7414 norm 0.0500
barSigmaiB 0.100 1.2063 0.9970 1.4157 norm 0.0500
barSigmaiG 0.100 1.1751 0.9859 1.3644 norm 0.0500
barSigmaUL 0.100 0.9586 0.9041 1.0130 norm 0.0500
standard deviation of measurement errors
prior mean post. mean 90% HPD interval prior pstdev
CapPY 0.051 0.0376 0.0019 0.0734 unif 0.0286
CapY 0.051 0.0522 0.0058 0.0987 unif 0.0286
Total computing time : 20h20
