hi all
I'm estimating an open macro economy model. I'm using the posterior mode optimization routine mode_compute=6. but modes values in posteriors are very higher than modes values in priors.Does the data need to be reviewed?
Thank you very much
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diffrence between modes in posteriors and priors
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diffrence between modes in posteriors and priors
by sara-sh » Sun Nov 16, 2014 10:17 am
- Attachments
-
sara.mod- Configuring Dynare ...
[mex] Generalized QZ.
[mex] Sylvester equation solution.
[mex] Kronecker products.
[mex] Sparse kronecker products.
[mex] Local state space iteration (second order).
[mex] Bytecode evaluation.
[mex] k-order perturbation solver.
[mex] k-order solution simulation.
[mex] Quasi Monte-Carlo sequence (Sobol).
[mex] Markov Switching SBVAR.
Starting Dynare (version 4.4.3).
Starting preprocessing of the model file ...
Found 7 equation(s).
Evaluating expressions...done
Computing static model derivatives:
- order 1
- order 2
- derivatives of Jacobian/Hessian w.r. to parameters
Computing dynamic model derivatives:
- order 1
- order 2
- derivatives of Jacobian/Hessian w.r. to parameters
Processing outputs ...done
Preprocessing completed.
Starting MATLAB/Octave computing.
STEADY-STATE RESULTS:
r 0
y 0
pie 0
e 0
y_s 0
pie_s 0
r_s 0
EIGENVALUES:
Modulus Real Imaginary
0.3325 0.3325 0
0.6349 0.6075 0.1845
0.6349 0.6075 -0.1845
0.9555 0.944 0.1474
0.9555 0.944 -0.1474
1.302 1.12 0.6636
1.302 1.12 -0.6636
1.467 1.467 0
3.14 3.016 0.8731
3.14 3.016 -0.8731
There are 5 eigenvalue(s) larger than 1 in modulus
for 5 forward-looking variable(s)
The rank condition is verified.
MODEL SUMMARY
Number of variables: 7
Number of stochastic shocks: 7
Number of state variables: 5
Number of jumpers: 5
Number of static variables: 2
MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS
Variables e_y e_pie e_e e_y_s e_pie_s e_r_s e_r
e_y 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_pie 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_e 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_y_s 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_pie_s 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_r_s 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_r 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000
POLICY AND TRANSITION FUNCTIONS
r y pie e y_s pie_s r_s
y(-1) 0.306815 0.554397 0.144712 0.516456 0 0 0
pie(-1) 0.144183 -0.019852 0.323375 0.078705 0 0 0
e(-1) -0.074387 -0.067375 -0.028813 0.669676 0 0 0
y_s(-1) 1.682626 0.040055 0.847971 0.765970 0.923804 0.174968 0.670496
pie_s(-1) 0.888782 0.381750 0.421653 -1.319423 -0.126536 0.964261 1.575752
e_y 0.613631 1.108795 0.289425 1.032912 0 0 0
e_pie 0.576732 -0.079410 1.293499 0.314819 0 0 0
e_e -0.026049 0.087008 0.000260 1.545934 0 0 0
e_y_s 2.243501 0.053407 1.130628 1.021294 1.231739 0.233291 0.893994
e_pie_s 1.185043 0.509000 0.562204 -1.759231 -0.168715 1.285682 2.101002
e_r_s -0.198301 -0.092349 -0.113323 -1.648063 -0.123174 -0.023329 0.910601
e_r 0.820544 -0.190190 -0.072096 1.287706 0 0 0
THEORETICAL MOMENTS
VARIABLE MEAN STD. DEV. VARIANCE
r 0.0000 0.0854 0.0073
y 0.0000 0.0342 0.0012
pie 0.0000 0.0158 0.0003
e 0.0000 0.1561 0.0244
y_s 0.0000 0.0000 0.0000
pie_s 0.0000 0.0000 0.0000
r_s 0.0000 0.0000 0.0000
VARIANCE DECOMPOSITION (in percent)
e_y e_pie e_e e_y_s e_pie_s e_r_s e_r
r 0.00 0.00 0.00 0.00 0.00 0.00 100.00
y 0.00 0.00 0.00 0.00 0.00 0.00 100.00
pie 0.00 0.00 0.00 0.00 0.00 0.00 100.00
e 0.00 0.00 0.00 0.00 0.00 0.00 100.00
y_s 0.00 0.00 0.00 57.59 41.83 0.58 0.00
pie_s 0.00 0.00 0.00 54.24 45.21 0.54 0.00
r_s 0.00 0.00 0.00 58.86 39.34 1.80 0.00
MATRIX OF CORRELATIONS
Variables r y pie e
r 1.0000 -0.3044 -0.1927 0.6464
y -0.3044 1.0000 0.9912 -0.8913
pie -0.1927 0.9912 1.0000 -0.8234
e 0.6464 -0.8913 -0.8234 1.0000
COEFFICIENTS OF AUTOCORRELATION
Order 1 2 3 4 5
r -0.1305 -0.1080 -0.0787 -0.0521 -0.0315
y 0.8192 0.5923 0.3894 0.2343 0.1278
pie 0.8670 0.6508 0.4414 0.2741 0.1550
e 0.5622 0.2800 0.1136 0.0251 -0.0152
Prior distribution for parameter rho_pie has two modes!
Warning: BETAINV did not converge for a = 0.397959, b = 0.132653, p = 0.999.
> In betainv at 61
In draw_prior_density at 47
In plot_priors at 55
In dynare_estimation_init at 257
In dynare_estimation_1 at 81
In dynare_estimation at 89
In sara at 250
In dynare at 180
Loading 64 observations from datasara2.xlsx
Initial value of the log posterior (or likelihood): -47219424101970.91
==========================================================
Change in the covariance matrix = 2371.2888.
Mode improvement = 47219324101970.91
New value of jscale = 0.5154
==========================================================
==========================================================
Change in the covariance matrix = 53759.1981.
Mode improvement = 85984441.3901
New value of jscale = 0.196
==========================================================
==========================================================
Change in the covariance matrix = 54788.0514.
Mode improvement = 12254075.3321
New value of jscale = 8.7057e-006
==========================================================
Optimal value of the scale parameter = 8.7057e-006
Final value of the log posterior (or likelihood): 1761483.2778
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
delta1 0.250 0.2563 0.0009 norm 0.1000
delta2 0.250 0.2583 0.0052 norm 0.1000
delta3 0.100 -0.2437 0.0056 norm 0.1000
lambda1 0.100 0.2675 0.0219 norm 0.1000
lambda2 0.010 -0.0160 0.0001 norm 0.0100
lambda_s 0.100 0.0000 0.0023 norm 0.1000
psi1 1.500 0.0000 0.0001 norm 0.3000
psi2 0.250 -0.0000 0.0000 norm 0.1000
delta_s 0.100 0.4834 0.0088 norm 0.1000
psi1_s 1.750 1.3966 0.0212 norm 0.3000
psi2_s 0.100 0.3290 0.0023 norm 0.1000
delta 0.500 0.7015 0.0014 beta 0.2500
rho_pie 0.750 0.4551 0.0007 beta 0.3500
rho_y 0.500 0.7498 0.0007 beta 0.2500
rho_y_s 0.250 0.7579 0.0042 beta 0.1500
rho_pie_s 0.250 0.5192 0.0004 beta 0.1500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_e 0.100 334.3093 0.1451 invg Inf
e_y 0.100 460.0395 1.5909 invg Inf
e_pie 1.000 64.7799 0.4992 invg 1.0000
e_y_s 0.010 202.9169 0.9108 invg Inf
e_pie_s 0.100 362.5677 1.0603 invg 1.0000
e_r_s 5.000 604.4423 1.9220 invg Inf
Log data density [Laplace approximation] is -1761627.771937.
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 sara/metropolis\sara_mh_history_0.mat
Estimation::mcmc: Number of mh files: 6 per block.
Estimation::mcmc: Total number of generated files: 12.
Estimation::mcmc: Total number of iterations: 30000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 58.5414%
Chain 2: 66.3678%
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!
Parameter 10... Done!
Parameter 11... Done!
Parameter 12... Done!
Parameter 13... Done!
Parameter 14... Done!
Parameter 15... Done!
Parameter 16... Done!
Parameter 17... Done!
Parameter 18... Done!
Parameter 19... Done!
Parameter 20... Done!
Parameter 21... Done!
Parameter 22... Done!
Estimation::mcmc: Total number of MH draws: 30000.
Estimation::mcmc: Total number of generated MH files: 6.
Estimation::mcmc: I'll use mh-files 2 to 6.
Estimation::mcmc: In MH-file number 2 I'll start at line 791.
Estimation::mcmc: Finally I keep 24000 draws.
Estimation::marginal density: I'm computing the posterior mean and covariance... Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In compute_mh_covariance_matrix at 77
In marginal_density at 53
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 59
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Done!
Estimation::marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 69
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: The support of the weighting density function is not large enough...
Estimation::marginal density: I increase the variance of this distribution.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 105
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
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
delta1 0.250 0.2564 0.2563 0.2565 norm 0.1000
delta2 0.250 0.2584 0.2582 0.2586 norm 0.1000
delta3 0.100 -0.2439 -0.2441 -0.2437 norm 0.1000
lambda1 0.100 0.2683 0.2673 0.2694 norm 0.1000
lambda2 0.010 -0.0160 -0.0160 -0.0159 norm 0.0100
lambda_s 0.100 0.0001 0.0000 0.0002 norm 0.1000
psi1 1.500 0.0000 0.0000 0.0000 norm 0.3000
psi2 0.250 -0.0000 -0.0000 0.0000 norm 0.1000
delta_s 0.100 0.4837 0.4834 0.4841 norm 0.1000
psi1_s 1.750 1.3954 1.3948 1.3960 norm 0.3000
psi2_s 0.100 0.3291 0.3288 0.3294 norm 0.1000
delta 0.500 0.7016 0.7014 0.7017 beta 0.2500
rho_pie 0.750 0.4552 0.4551 0.4553 beta 0.3500
rho_y 0.500 0.7497 0.7495 0.7499 beta 0.2500
rho_y_s 0.250 0.7581 0.7578 0.7583 beta 0.1500
rho_pie_s 0.250 0.5191 0.5191 0.5192 beta 0.1500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_e 0.100 334.3417 334.2872 334.3950 invg Inf
e_y 0.100 460.1055 460.0549 460.1713 invg Inf
e_pie 1.000 64.7634 64.7291 64.7930 invg 1.0000
e_y_s 0.010 202.9618 202.9227 203.0096 invg Inf
e_pie_s 0.100 362.6095 362.5602 362.6612 invg 1.0000
e_r_s 5.000 604.5080 604.4266 604.5939 invg Inf
Warning: BETAINV did not converge for a = 0.397959, b = 0.132653, p = 0.999.
> In betainv at 61
In draw_prior_density at 47
In PlotPosteriorDistributions at 80
In dynare_estimation_1 at 804
In dynare_estimation at 89
In sara at 250
In dynare at 180
Prior distribution for parameter rho_pie has two modes!
Loading 64 observations from datasara2.xlsx
==== Identification analysis ====
Prior distribution for parameter rho_pie has two modes!
Testing posterior mean
Evaluating simulated moment uncertainty ... please wait
Doing 231 replicas of length 300 periods.
Simulated moment uncertainty ... done!
All parameters are identified in the model (rank of H).
All parameters are identified by J moments (rank of J)
Press ENTER to print advanced diagnostics
Collinearity patterns with 1 parameter(s)
Parameter [ Expl. params ] cosn
e_e [ e_y ] 0.5174067
e_y [ rho_y ] 0.6521608
e_pie [ e_e ] 0.4733903
e_y_s [ delta_s ] 0.4997182
e_pie_s [ lambda_s ] 0.9857905
e_r_s [ e_e ] 0.4639640
delta1 [ rho_y ] 0.8513871
delta2 [ delta3 ] 0.9994066
delta3 [ delta2 ] 0.9994066
lambda1 [ rho_y ] 0.6758951
lambda2 [ rho_pie ] 0.8345356
lambda_s [ rho_pie_s ] 0.9999496
psi1 [ lambda1 ] 0.3817920
psi2 [ delta ] 0.5390813
delta_s [ rho_y_s ] 0.9809222
psi1_s [ psi2_s ] 0.9999314
psi2_s [ psi1_s ] 0.9999314
delta [ delta3 ] 0.8482000
rho_pie [ lambda2 ] 0.8345356
rho_y [ delta1 ] 0.8513871
rho_y_s [ delta_s ] 0.9809222
rho_pie_s [ lambda_s ] 0.9999496
Collinearity patterns with 2 parameter(s)
Parameter [ Expl. params ] cosn
e_e [ delta1 psi2 ] 0.6620731
e_y [ delta rho_y ] 0.7474120
e_pie [ e_e rho_pie ] 0.5410584
e_y_s [ delta3 rho_y_s ] 0.5343593
e_pie_s [ lambda_s rho_pie_s ] 0.9999815
e_r_s [ e_e rho_pie ] 0.5459235
delta1 [ delta2 delta3 ] 0.9855182
delta2 [ delta1 delta3 ] 0.9999533
delta3 [ delta1 delta2 ] 0.9999489
lambda1 [ delta rho_pie ] 0.8573036
lambda2 [ psi1 rho_pie ] 0.8511416
lambda_s [ e_pie_s rho_pie_s ] 0.9999999
psi1 [ lambda1 delta ] 0.4398955
psi2 [ e_e delta ] 0.6932882
delta_s [ delta3 rho_y_s ] 0.9830048
psi1_s [ psi2_s rho_y_s ] 0.9999483
psi2_s [ psi1_s rho_y_s ] 0.9999495
delta [ delta3 psi2 ] 0.9148945
rho_pie [ lambda1 delta ] 0.8926314
rho_y [ delta1 lambda1 ] 0.9138594
rho_y_s [ delta3 delta_s ] 0.9848866
rho_pie_s [ e_pie_s lambda_s ] 0.9999999
Press ENTER to plot advanced diagnostics
==== Identification analysis completed ====
Total computing time : 1h36m00s
Note: warning(s) encountered in MATLAB/Octave code
>> dynare sara
Configuring Dynare ...
[mex] Generalized QZ.
[mex] Sylvester equation solution.
[mex] Kronecker products.
[mex] Sparse kronecker products.
[mex] Local state space iteration (second order).
[mex] Bytecode evaluation.
[mex] k-order perturbation solver.
[mex] k-order solution simulation.
[mex] Quasi Monte-Carlo sequence (Sobol).
[mex] Markov Switching SBVAR.
Starting Dynare (version 4.4.3).
Starting preprocessing of the model file ...
Found 7 equation(s).
Evaluating expressions...done
Computing static model derivatives:
- order 1
- order 2
- derivatives of Jacobian/Hessian w.r. to parameters
Computing dynamic model derivatives:
- order 1
- order 2
- derivatives of Jacobian/Hessian w.r. to parameters
Processing outputs ...done
Preprocessing completed.
Starting MATLAB/Octave computing.
STEADY-STATE RESULTS:
r 0
y 0
pie 0
e 0
y_s 0
pie_s 0
r_s 0
EIGENVALUES:
Modulus Real Imaginary
0.3325 0.3325 0
0.6349 0.6075 0.1845
0.6349 0.6075 -0.1845
0.9555 0.944 0.1474
0.9555 0.944 -0.1474
1.302 1.12 0.6636
1.302 1.12 -0.6636
1.467 1.467 0
3.14 3.016 0.8731
3.14 3.016 -0.8731
There are 5 eigenvalue(s) larger than 1 in modulus
for 5 forward-looking variable(s)
The rank condition is verified.
MODEL SUMMARY
Number of variables: 7
Number of stochastic shocks: 7
Number of state variables: 5
Number of jumpers: 5
Number of static variables: 2
MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS
Variables e_y e_pie e_e e_y_s e_pie_s e_r_s e_r
e_y 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_pie 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_e 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_y_s 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_pie_s 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_r_s 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
e_r 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000
POLICY AND TRANSITION FUNCTIONS
r y pie e y_s pie_s r_s
y(-1) 0.306815 0.554397 0.144712 0.516456 0 0 0
pie(-1) 0.144183 -0.019852 0.323375 0.078705 0 0 0
e(-1) -0.074387 -0.067375 -0.028813 0.669676 0 0 0
y_s(-1) 1.682626 0.040055 0.847971 0.765970 0.923804 0.174968 0.670496
pie_s(-1) 0.888782 0.381750 0.421653 -1.319423 -0.126536 0.964261 1.575752
e_y 0.613631 1.108795 0.289425 1.032912 0 0 0
e_pie 0.576732 -0.079410 1.293499 0.314819 0 0 0
e_e -0.026049 0.087008 0.000260 1.545934 0 0 0
e_y_s 2.243501 0.053407 1.130628 1.021294 1.231739 0.233291 0.893994
e_pie_s 1.185043 0.509000 0.562204 -1.759231 -0.168715 1.285682 2.101002
e_r_s -0.198301 -0.092349 -0.113323 -1.648063 -0.123174 -0.023329 0.910601
e_r 0.820544 -0.190190 -0.072096 1.287706 0 0 0
THEORETICAL MOMENTS
VARIABLE MEAN STD. DEV. VARIANCE
r 0.0000 0.0854 0.0073
y 0.0000 0.0342 0.0012
pie 0.0000 0.0158 0.0003
e 0.0000 0.1561 0.0244
y_s 0.0000 0.0000 0.0000
pie_s 0.0000 0.0000 0.0000
r_s 0.0000 0.0000 0.0000
VARIANCE DECOMPOSITION (in percent)
e_y e_pie e_e e_y_s e_pie_s e_r_s e_r
r 0.00 0.00 0.00 0.00 0.00 0.00 100.00
y 0.00 0.00 0.00 0.00 0.00 0.00 100.00
pie 0.00 0.00 0.00 0.00 0.00 0.00 100.00
e 0.00 0.00 0.00 0.00 0.00 0.00 100.00
y_s 0.00 0.00 0.00 57.59 41.83 0.58 0.00
pie_s 0.00 0.00 0.00 54.24 45.21 0.54 0.00
r_s 0.00 0.00 0.00 58.86 39.34 1.80 0.00
MATRIX OF CORRELATIONS
Variables r y pie e
r 1.0000 -0.3044 -0.1927 0.6464
y -0.3044 1.0000 0.9912 -0.8913
pie -0.1927 0.9912 1.0000 -0.8234
e 0.6464 -0.8913 -0.8234 1.0000
COEFFICIENTS OF AUTOCORRELATION
Order 1 2 3 4 5
r -0.1305 -0.1080 -0.0787 -0.0521 -0.0315
y 0.8192 0.5923 0.3894 0.2343 0.1278
pie 0.8670 0.6508 0.4414 0.2741 0.1550
e 0.5622 0.2800 0.1136 0.0251 -0.0152
Prior distribution for parameter rho_pie has two modes!
Warning: BETAINV did not converge for a = 0.397959, b = 0.132653, p = 0.999.
> In betainv at 61
In draw_prior_density at 47
In plot_priors at 55
In dynare_estimation_init at 257
In dynare_estimation_1 at 81
In dynare_estimation at 89
In sara at 250
In dynare at 180
Loading 64 observations from datasara2.xlsx
Initial value of the log posterior (or likelihood): -47219424101970.91
==========================================================
Change in the covariance matrix = 2371.2888.
Mode improvement = 47219324101970.91
New value of jscale = 0.5154
==========================================================
==========================================================
Change in the covariance matrix = 53759.1981.
Mode improvement = 85984441.3901
New value of jscale = 0.196
==========================================================
==========================================================
Change in the covariance matrix = 54788.0514.
Mode improvement = 12254075.3321
New value of jscale = 8.7057e-006
==========================================================
Optimal value of the scale parameter = 8.7057e-006
Final value of the log posterior (or likelihood): 1761483.2778
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
delta1 0.250 0.2563 0.0009 norm 0.1000
delta2 0.250 0.2583 0.0052 norm 0.1000
delta3 0.100 -0.2437 0.0056 norm 0.1000
lambda1 0.100 0.2675 0.0219 norm 0.1000
lambda2 0.010 -0.0160 0.0001 norm 0.0100
lambda_s 0.100 0.0000 0.0023 norm 0.1000
psi1 1.500 0.0000 0.0001 norm 0.3000
psi2 0.250 -0.0000 0.0000 norm 0.1000
delta_s 0.100 0.4834 0.0088 norm 0.1000
psi1_s 1.750 1.3966 0.0212 norm 0.3000
psi2_s 0.100 0.3290 0.0023 norm 0.1000
delta 0.500 0.7015 0.0014 beta 0.2500
rho_pie 0.750 0.4551 0.0007 beta 0.3500
rho_y 0.500 0.7498 0.0007 beta 0.2500
rho_y_s 0.250 0.7579 0.0042 beta 0.1500
rho_pie_s 0.250 0.5192 0.0004 beta 0.1500
standard deviation of shocks
prior mean mode s.d. prior pstdev
e_e 0.100 334.3093 0.1451 invg Inf
e_y 0.100 460.0395 1.5909 invg Inf
e_pie 1.000 64.7799 0.4992 invg 1.0000
e_y_s 0.010 202.9169 0.9108 invg Inf
e_pie_s 0.100 362.5677 1.0603 invg 1.0000
e_r_s 5.000 604.4423 1.9220 invg Inf
Log data density [Laplace approximation] is -1761627.771937.
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 sara/metropolis\sara_mh_history_0.mat
Estimation::mcmc: Number of mh files: 6 per block.
Estimation::mcmc: Total number of generated files: 12.
Estimation::mcmc: Total number of iterations: 30000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 58.5414%
Chain 2: 66.3678%
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!
Parameter 10... Done!
Parameter 11... Done!
Parameter 12... Done!
Parameter 13... Done!
Parameter 14... Done!
Parameter 15... Done!
Parameter 16... Done!
Parameter 17... Done!
Parameter 18... Done!
Parameter 19... Done!
Parameter 20... Done!
Parameter 21... Done!
Parameter 22... Done!
Estimation::mcmc: Total number of MH draws: 30000.
Estimation::mcmc: Total number of generated MH files: 6.
Estimation::mcmc: I'll use mh-files 2 to 6.
Estimation::mcmc: In MH-file number 2 I'll start at line 791.
Estimation::mcmc: Finally I keep 24000 draws.
Estimation::marginal density: I'm computing the posterior mean and covariance... Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In compute_mh_covariance_matrix at 77
In marginal_density at 53
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 59
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Done!
Estimation::marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 69
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: The support of the weighting density function is not large enough...
Estimation::marginal density: I increase the variance of this distribution.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 105
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
Estimation::marginal density: Let me try again.
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.899781e-016.
> In marginal_density at 111
In dynare_estimation_1 at 799
In dynare_estimation at 89
In sara at 250
In dynare at 180
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
delta1 0.250 0.2564 0.2563 0.2565 norm 0.1000
delta2 0.250 0.2584 0.2582 0.2586 norm 0.1000
delta3 0.100 -0.2439 -0.2441 -0.2437 norm 0.1000
lambda1 0.100 0.2683 0.2673 0.2694 norm 0.1000
lambda2 0.010 -0.0160 -0.0160 -0.0159 norm 0.0100
lambda_s 0.100 0.0001 0.0000 0.0002 norm 0.1000
psi1 1.500 0.0000 0.0000 0.0000 norm 0.3000
psi2 0.250 -0.0000 -0.0000 0.0000 norm 0.1000
delta_s 0.100 0.4837 0.4834 0.4841 norm 0.1000
psi1_s 1.750 1.3954 1.3948 1.3960 norm 0.3000
psi2_s 0.100 0.3291 0.3288 0.3294 norm 0.1000
delta 0.500 0.7016 0.7014 0.7017 beta 0.2500
rho_pie 0.750 0.4552 0.4551 0.4553 beta 0.3500
rho_y 0.500 0.7497 0.7495 0.7499 beta 0.2500
rho_y_s 0.250 0.7581 0.7578 0.7583 beta 0.1500
rho_pie_s 0.250 0.5191 0.5191 0.5192 beta 0.1500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
e_e 0.100 334.3417 334.2872 334.3950 invg Inf
e_y 0.100 460.1055 460.0549 460.1713 invg Inf
e_pie 1.000 64.7634 64.7291 64.7930 invg 1.0000
e_y_s 0.010 202.9618 202.9227 203.0096 invg Inf
e_pie_s 0.100 362.6095 362.5602 362.6612 invg 1.0000
e_r_s 5.000 604.5080 604.4266 604.5939 invg Inf
Warning: BETAINV did not converge for a = 0.397959, b = 0.132653, p = 0.999.
> In betainv at 61
In draw_prior_density at 47
In PlotPosteriorDistributions at 80
In dynare_estimation_1 at 804
In dynare_estimation at 89
In sara at 250
In dynare at 180
Prior distribution for parameter rho_pie has two modes!
Loading 64 observations from datasara2.xlsx
==== Identification analysis ====
Prior distribution for parameter rho_pie has two modes!
Testing posterior mean
Evaluating simulated moment uncertainty ... please wait
Doing 231 replicas of length 300 periods.
Simulated moment uncertainty ... done!
All parameters are identified in the model (rank of H).
All parameters are identified by J moments (rank of J)
Press ENTER to print advanced diagnostics
Collinearity patterns with 1 parameter(s)
Parameter [ Expl. params ] cosn
e_e [ e_y ] 0.5174067
e_y [ rho_y ] 0.6521608
e_pie [ e_e ] 0.4733903
e_y_s [ delta_s ] 0.4997182
e_pie_s [ lambda_s ] 0.9857905
e_r_s [ e_e ] 0.4639640
delta1 [ rho_y ] 0.8513871
delta2 [ delta3 ] 0.9994066
delta3 [ delta2 ] 0.9994066
lambda1 [ rho_y ] 0.6758951
lambda2 [ rho_pie ] 0.8345356
lambda_s [ rho_pie_s ] 0.9999496
psi1 [ lambda1 ] 0.3817920
psi2 [ delta ] 0.5390813
delta_s [ rho_y_s ] 0.9809222
psi1_s [ psi2_s ] 0.9999314
psi2_s [ psi1_s ] 0.9999314
delta [ delta3 ] 0.8482000
rho_pie [ lambda2 ] 0.8345356
rho_y [ delta1 ] 0.8513871
rho_y_s [ delta_s ] 0.9809222
rho_pie_s [ lambda_s ] 0.9999496
Collinearity patterns with 2 parameter(s)
Parameter [ Expl. params ] cosn
e_e [ delta1 psi2 ] 0.6620731
e_y [ delta rho_y ] 0.7474120
e_pie [ e_e rho_pie ] 0.5410584
e_y_s [ delta3 rho_y_s ] 0.5343593
e_pie_s [ lambda_s rho_pie_s ] 0.9999815
e_r_s [ e_e rho_pie ] 0.5459235
delta1 [ delta2 delta3 ] 0.9855182
delta2 [ delta1 delta3 ] 0.9999533
delta3 [ delta1 delta2 ] 0.9999489
lambda1 [ delta rho_pie ] 0.8573036
lambda2 [ psi1 rho_pie ] 0.8511416
lambda_s [ e_pie_s rho_pie_s ] 0.9999999
psi1 [ lambda1 delta ] 0.4398955
psi2 [ e_e delta ] 0.6932882
delta_s [ delta3 rho_y_s ] 0.9830048
psi1_s [ psi2_s rho_y_s ] 0.9999483
psi2_s [ psi1_s rho_y_s ] 0.9999495
delta [ delta3 psi2 ] 0.9148945
rho_pie [ lambda1 delta ] 0.8926314
rho_y [ delta1 lambda1 ] 0.9138594
rho_y_s [ delta3 delta_s ] 0.9848866
rho_pie_s [ e_pie_s lambda_s ] 0.9999999
Press ENTER to plot advanced diagnostics
==== Identification analysis completed ====
Total computing time : 1h36m00s
Note: warning(s) encountered in MATLAB/Octave code - (1.97 KiB) Downloaded 36 times
- sara-sh
- Posts: 2
- Joined: Thu Nov 13, 2014 11:16 am
Re: diffrence between modes in posteriors and priors
by jpfeifer » Sun Nov 16, 2014 1:02 pm
Given the large posterior shock variances, there is most likely something wrong with the observation equation. See Pfeifer(2013): "A Guide to Specifying Observation Equations for the Estimation of DSGE Models" https://sites.google.com/site/pfeiferecon/Pfeifer_2013_Observation_Equations.pdf on how to correctly map data and model variables.
- jpfeifer
- Posts: 6940
- Joined: Sun Feb 21, 2010 4:02 pm
- Location: Cologne, Germany
Re: diffrence between modes in posteriors and priors
by sara-sh » Mon Nov 17, 2014 9:40 am
Thank you very much
- sara-sh
- Posts: 2
- Joined: Thu Nov 13, 2014 11:16 am
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