Gali & Monacelli 2016

Estimation
1

Dear professor,
I replicated Gali, Monacelli (2016) with some features( like Flexible interest rule and foreign price differ from 0) from Gali (2015) (chapter 8), by using your code, using Romania data.
The problem is that I don’t know exactly what if I should use the first difference of the logarithm or to trend/demean the logarithm variables, because the result somehow differs.
Also I want to introduce Inflation of wages or real wage as an observable variable of the model, but it not work if the employment is observable.
Put the data and the model here.
date.m (928 Bytes)
datedif.m (821 Bytes)
date.xlsx (72.9 KB)
GM_2016_6.mod (6.5 KB)

Thank you

2

What exactly are you trying to do here? The file you uploaded does not contain any estimation-related codes.

3

Estimation command is not in the model?

I send you, the new version of the model baseline Gali&Monacelli 2016 attempt. I want to put an Flexible CPI inflation targeting with a monetary policy shock and to put the foreign price as an exogenous shock for the terms of trade.

Also how I can export the results to in excel?

GM2016bun.mod (5.8 KB)
date5.m (1.2 KB)
date_actuale7.xlsx (194.9 KB)
Thank you for response

4

I still don’t understand. Nothing you describe has anything to do with estimation. And you did not tell us which results you want to export to Excel.

5

Sorry for not understanding. I want to export all the tables from the estimation in to excel or a table for pdf or doc.
My estimation results are for the last version are:

ESTIMATION RESULTS

Log data density is 749.039708.

parameters
prior mean post. mean 95% HPD interval prior pstdev

phi 1.500 3.3993 2.6526 4.1640 norm 0.5000
phi_pi 1.700 2.0227 1.6441 2.3904 norm 0.2000
phi_i 0.600 0.9775 0.9729 0.9800 beta 0.1000
rho_a 0.600 0.6655 0.5658 0.7625 beta 0.1000
rho_z 0.600 0.4550 0.3105 0.6059 beta 0.1000
rho_z_1 0.600 0.8884 0.7963 0.9627 beta 0.1000
rho_z_2 0.600 0.4004 0.2386 0.5740 beta 0.1000
rho_z_3 0.600 0.4968 0.3154 0.6547 beta 0.1000
rho_tau 0.600 0.5096 0.4014 0.6067 beta 0.1000
rho_nu 0.600 0.5212 0.4131 0.6148 beta 0.1000
epsilon_p 3.800 3.8016 2.2611 5.4626 gamm 0.8000
epsilon_w 4.300 3.8438 2.4302 5.3264 gamm 0.8000
theta_p 0.500 0.6801 0.6206 0.7395 beta 0.1500
theta_w 0.500 0.0774 0.0353 0.1253 beta 0.1500

standard deviation of shocks
prior mean post. mean 95% HPD interval prior pstdev

eps_z 0.010 0.0426 0.0324 0.0519 invg Inf
eps_z_1 0.010 0.1133 0.0903 0.1391 invg Inf
eps_z_2 0.010 0.0398 0.0310 0.0499 invg Inf
eps_a 0.010 0.0593 0.0403 0.0809 invg Inf
eps_tau 0.010 0.2751 0.1985 0.3550 invg Inf
eps_nu 0.010 0.0030 0.0023 0.0039 invg Inf
eps_z_3 0.001 0.0593 0.0482 0.0714 invg Inf
Estimation::mcmc: Forecasted variables (mean)
Estimation::mcmc: Forecasted variables (mean), done!
Estimation::mcmc: Forecasted variables (point)
Estimation::mcmc: Forecasted variables (point), done!
MODEL_DIAGNOSTICS: No obvious problems with this mod-file were detected.

MODEL SUMMARY

Number of variables: 26
Number of stochastic shocks: 7
Number of state variables: 10
Number of jumpers: 4
Number of static variables: 12

MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS
Variables eps_z eps_z_1 eps_z_2 eps_a eps_tau eps_nu eps_z_3
eps_z 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
eps_z_1 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000
eps_z_2 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000
eps_a 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000
eps_tau 0.000000 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000
eps_nu 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000 0.000000
eps_z_3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000

POLICY AND TRANSITION FUNCTIONS
y y_gap pi_p pi pi_w d_e s i omega c n
a(-1) 0.393160 -0.272300 -0.047500 0.149080 -0.280078 0.345660 0.393160 -0.014067 -0.429159 0.196580 -0.378195
z(-1) 0.100082 0.137317 0.004374 -0.059340 0.263366 -0.123054 -0.127428 0.007816 0.322706 0.391305 0.139002
i(-1) -4.429695 -4.429695 -2.356272 -4.571120 -12.716394 -6.785967 -4.429695 0.429663 -8.145274 -2.214848 -6.152354
omega(-1) 0.001516 0.001516 0.035485 0.036243 -0.557907 0.037001 0.001516 0.001768 0.405850 0.000758 0.002105
s(-1) 0.104062 0.104062 0.072693 -0.375276 0.017603 -0.823245 0.104062 -0.009136 0.392879 0.052031 0.144530
z_1_star(-1) 0.170493 0.097795 0.027924 -0.108927 0.234242 -0.245778 -0.273702 0.002527 0.343169 -0.136851 0.236796
z_2_star(-1) 0.116230 0.083464 0.003344 0.161561 0.148530 0.319779 0.316435 0.013761 -0.013031 -0.242193 0.161430
tau(-1) -0.041321 0.042077 0.064723 0.044063 -0.096498 0.023402 -0.041321 0.005233 -0.140561 -0.020661 -0.057391
nu(-1) -4.473653 -4.473653 -2.660248 -4.897075 -13.378006 -7.133901 -4.473653 -0.044830 -8.480931 -2.236827 -6.213407
z_3_star(-1) 0.025618 -0.015036 0.015122 0.027931 0.076447 -0.456058 0.025618 0.000121 0.048516 0.012809 0.035580
eps_z 0.219951 0.301782 0.009612 -0.130413 0.578803 -0.270437 -0.280049 0.017177 0.709216 0.859975 0.305487
eps_z_1 0.191912 0.110081 0.031432 -0.122611 0.263670 -0.276655 -0.308088 0.002844 0.386281 -0.154044 0.266545
eps_z_2 0.290276 0.208445 0.008351 0.403489 0.370944 0.798627 0.790276 0.034366 -0.032545 -0.604862 0.403162
eps_a 0.590809 -0.409191 -0.071379 0.224026 -0.420879 0.519430 0.590809 -0.021138 -0.644905 0.295405 -0.568321
eps_tau -0.081090 0.082573 0.127015 0.086470 -0.189371 0.045924 -0.081090 0.010269 -0.275841 -0.040545 -0.112625
eps_nu 8.583446 8.583446 5.104127 9.395850 25.667922 13.687572 8.583446 0.086014 16.272073 4.291723 11.921452
eps_z_3 0.051566 -0.030266 0.030438 0.056221 0.153880 -0.917996 0.051566 0.000244 0.097659 0.025783 0.071619

THEORETICAL MOMENTS
VARIABLE MEAN STD. DEV. VARIANCE
y 0.0000 1.0501 1.1026
y_gap 0.0000 1.0454 1.0929
pi_p 0.0000 0.6881 0.4736
pi 0.0000 0.9705 0.9418
pi_w 0.0000 2.6737 7.1485
d_e 0.0000 1.3783 1.8997
s 0.0000 1.0565 1.1162
i 0.0000 0.0130 0.0002
omega 0.0000 2.9515 8.7113
c 0.0000 0.5387 0.2902
n 0.0000 1.4525 2.1097

VARIANCE DECOMPOSITION (in percent)
eps_z eps_z_1 eps_z_2 eps_a eps_tau eps_nu eps_z_3
y 0.05 0.07 0.08 0.99 0.02 98.78 0.00
y_gap 0.09 0.01 0.04 0.17 0.01 99.67 0.00
pi_p 0.01 0.01 0.01 0.06 0.04 99.87 0.00
pi 0.02 0.02 0.28 0.06 0.01 99.61 0.00
pi_w 0.08 0.01 0.04 0.07 0.01 99.79 0.00
d_e 0.04 0.04 0.53 0.14 0.00 98.63 0.62
s 0.16 0.64 0.62 0.98 0.02 97.59 0.00
i 5.94 3.57 10.99 18.39 1.69 59.42 0.00
omega 0.08 0.05 0.00 0.09 0.05 99.72 0.00
c 3.00 0.61 1.60 0.94 0.02 93.82 0.00
n 0.05 0.07 0.08 0.17 0.02 99.60 0.00

MATRIX OF CORRELATIONS
Variables y y_gap pi_p pi pi_w d_e s i omega c n
y 1.0000 0.9914 0.9842 0.9300 0.7831 0.8177 0.9919 0.7099 0.9110 0.9707 0.9918
y_gap 0.9914 1.0000 0.9885 0.9315 0.7862 0.8185 0.9842 0.7597 0.9166 0.9667 0.9996
pi_p 0.9842 0.9885 1.0000 0.8808 0.6906 0.7405 0.9785 0.7279 0.9632 0.9591 0.9878
pi 0.9300 0.9315 0.8808 1.0000 0.9218 0.9680 0.9269 0.7276 0.7332 0.9012 0.9313
pi_w 0.7831 0.7862 0.6906 0.9218 1.0000 0.9529 0.7781 0.5875 0.4757 0.7636 0.7861
d_e 0.8177 0.8185 0.7405 0.9680 0.9529 1.0000 0.8163 0.6611 0.5512 0.7898 0.8178
s 0.9919 0.9842 0.9785 0.9269 0.7781 0.8163 1.0000 0.6933 0.9019 0.9571 0.9838
i 0.7099 0.7597 0.7279 0.7276 0.5875 0.6611 0.6933 1.0000 0.6835 0.6577 0.7611
omega 0.9110 0.9166 0.9632 0.7332 0.4757 0.5512 0.9019 0.6835 1.0000 0.8901 0.9168
c 0.9707 0.9667 0.9591 0.9012 0.7636 0.7898 0.9571 0.6577 0.8901 1.0000 0.9627
n 0.9918 0.9996 0.9878 0.9313 0.7861 0.8178 0.9838 0.7611 0.9168 0.9627 1.0000

COEFFICIENTS OF AUTOCORRELATION
Order 1 2 3 4 5
y 0.5704 0.3071 0.1579 0.0781 0.0373
y_gap 0.5672 0.3031 0.1542 0.0750 0.0350
pi_p 0.6675 0.4034 0.2280 0.1225 0.0632
pi 0.2216 0.1366 0.0791 0.0436 0.0232
pi_w 0.1558 -0.1020 -0.1396 -0.1109 -0.0734
d_e 0.0078 0.0032 0.0021 0.0017 0.0014
s 0.5718 0.3098 0.1612 0.0816 0.0407
i 0.6111 0.4551 0.3498 0.2603 0.1860
omega 0.8102 0.5430 0.3287 0.1858 0.0998
c 0.5659 0.3044 0.1576 0.0794 0.0395
n 0.5674 0.3034 0.1545 0.0753 0.0352

Taylor Rule of Inflation for Consumer Price Index Properties
All Shocks
sigma(y) 1.050
sigma(tilde y) 1.045
sigma(pi_p) 0.688
sigma(pi) 0.970
sigma(s) 1.056
sigma(d_e) 1.378
sigma(pi_w) 2.674
L 0.300
Total computing time : 0h05m33s

6

It’s easiest to use Dynare’s LaTeX-cababilities. See e.g. https://github.com/DynareTeam/dynare/blob/master/tests/TeX/fs2000_corr_ME.mod