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Various changes in tests/dsge-var mod files. 

+ Changed comments.
+ Use new Dynare's interface for specifying the version of numgrad.
time-shift
Stéphane Adjemian (Charybdis) 2013-11-17 12:55:56 +01:00
parent a96d361599
commit 823ed85e8d
2 changed files with 46 additions and 37 deletions
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39
tests/dsge-var/dsgevar_forward_calibrated_lambda.mod
View File

@ -1,10 +1,10 @@
//$ Declaration of the endogenous variables of the DSGE model.
// Declaration of the endogenous variables of the DSGE model.
var a g mc mrs n winf pie r rw y;
//$ Declaration of the exogenous variables of the DSGE model.
// Declaration of the exogenous variables of the DSGE model.
varexo e_a e_g e_lam e_ms;
//$ Declaration of the deep parameters
// Declaration of the deep parameters
parameters invsig delta gam rho gampie gamy rhoa rhog bet
thetabig omega eps ;
@ -22,7 +22,6 @@ rhog=0.5;
rho=0.5;
//$ Specification of the DSGE model used as a prior of the VAR model.
model(linear);
y=y(+1)-(1/invsig)*(r-pie(+1)+g(+1)-g);
@ -35,17 +34,17 @@ model(linear);
g=rhog*g(-1)+e_g;
rw=mrs;
//$ HYBRID PHILLIPS CURVED USED FOR THE SUMULATIONS:
// HYBRID PHILLIPS CURVED USED FOR THE SUMULATIONS:
// pie = (omega/(1+omega*bet))*pie(-1)+(bet/(1+omega*bet))*pie(1)+(1-delta)*
// (1-(1-1/thetabig)*bet)*(1-(1-1/thetabig))/((1-1/thetabig)*(1+delta*(eps-1)))/(1+omega*bet)*(mc+e_lam);
//$ FORWARD LOOKING PHILLIPS CURVE:
// FORWARD LOOKING PHILLIPS CURVE:
pie=bet*pie(+1)+(1-delta)*(1-(1-1/thetabig)*bet)*(1-(1-1/thetabig))/((1-1/thetabig)*(1+delta*(eps-1)))*(mc+e_lam);
end;
//$ Declaration of the prior beliefs about the deep parameters.
// Declaration of the prior beliefs about the deep parameters.
estimated_params;
stderr e_a, uniform_pdf,,,0,2;
stderr e_g, uniform_pdf,,,0,2;
@ -61,20 +60,24 @@ estimated_params;
rhog, uniform_pdf,,,0,1;
thetabig, gamma_pdf, 3, 1.42, 1, ;
//$Parameter for the hybrid Phillips curve
//omega, uniform_pdf,,,0,1;
// Parameter for the hybrid Phillips curve
// omega, uniform_pdf,,,0,1;
end;
//$ Declaration of the observed endogenous variables. Note that they are the variables of the VAR (4 by default) and that we must
//$ have as many observed variables as exogenous variables.
/*
** Declaration of the observed endogenous variables. Note that they are the variables of the VAR (4 by default) and that we must
** have as many observed variables as exogenous variables.
*/
varobs pie r rw y;
options_.gradient_method = 3;
//$ The option dsge_var=.8 triggers the estimation of a DSGE-VAR model, with a calibrated dsge prior weight equal to .8.
//$ The option bayesian_irf triggers the computation of the DSGE-VAR and DSGE posterior distribution of the IRFs.
//$ The Dashed lines are the first, fifth (ie the median) and ninth posterior deciles of the DSGE-VAR's IRFs, the bold dark curve is the
//$ posterior median of the DSGE's IRfs and the shaded surface covers the space between the first and ninth posterior deciles of the DSGE's IRFs.
estimation(datafile=datarabanal_hybrid,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var=.8,mode_compute=4,mh_replic=2000,bayesian_irf);
/* REMARK 1.
** The option dsge_var=.8 triggers the estimation of a DSGE-VAR model, with a calibrated dsge prior weight equal to .8.
**
** REMARK 2.
** The option bayesian_irf triggers the computation of the DSGE-VAR and DSGE posterior distribution of the IRFs.
** The Dashed lines are the first, fifth (ie the median) and ninth posterior deciles of the DSGE-VAR's IRFs, the bold dark curve is the
** posterior median of the DSGE's IRfs and the shaded surface covers the space between the first and ninth posterior deciles of the DSGE's IRFs.
*/
estimation(datafile=datarabanal_hybrid,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var=.8,optim=('NumgradAlgorithm',3),mode_compute=4,mh_replic=2000,bayesian_irf);

44
tests/dsge-var/dsgevar_forward_estimated_lambda.mod
View File

@ -1,10 +1,10 @@
//$ Declaration of the endogenous variables of the DSGE model.
// Declaration of the endogenous variables of the DSGE model.
var a g mc mrs n winf pie r rw y;
//$ Declaration of the exogenous variables of the DSGE model.
// Declaration of the exogenous variables of the DSGE model.
varexo e_a e_g e_lam e_ms;
//$ Declaration of the deep parameters
// Declaration of the deep parameters
parameters invsig delta gam rho gampie gamy rhoa rhog bet
thetabig omega eps ;
@ -21,8 +21,6 @@ rhoa=0.5;
rhog=0.5;
rho=0.5;
//$ Specification of the DSGE model used as a prior of the VAR model.
model(linear);
y=y(+1)-(1/invsig)*(r-pie(+1)+g(+1)-g);
@ -35,19 +33,20 @@ model(linear);
g=rhog*g(-1)+e_g;
rw=mrs;
//$ HYBRID PHILLIPS CURVED USED FOR THE SUMULATIONS:
// HYBRID PHILLIPS CURVED USED FOR THE SUMULATIONS:
// pie = (omega/(1+omega*bet))*pie(-1)+(bet/(1+omega*bet))*pie(1)+(1-delta)*
// (1-(1-1/thetabig)*bet)*(1-(1-1/thetabig))/((1-1/thetabig)*(1+delta*(eps-1)))/(1+omega*bet)*(mc+e_lam);
//$ FORWARD LOOKING PHILLIPS CURVE:
// FORWARD LOOKING PHILLIPS CURVE:
pie=bet*pie(+1)+(1-delta)*(1-(1-1/thetabig)*bet)*(1-(1-1/thetabig))/((1-1/thetabig)*(1+delta*(eps-1)))*(mc+e_lam);
end;
//$ Declaration of the prior beliefs about the deep parameters and the weight of the DSGE prior.
//$ The declaration of the estimated parameters dsge_prior_weight triggers the estimation of a DSGE-VAR model.
//$ Note that dsge_prior_weight is not declared as a parameter at the top of the mod file.
/*
** Declaration of the prior beliefs about the deep parameters AND the weight of the DSGE prior.
** Note that dsge_prior_weight is not declared as a parameter at the top of the mod file.
*/
estimated_params;
stderr e_a, uniform_pdf,,,0,2;
stderr e_g, uniform_pdf,,,0,2;
@ -63,20 +62,27 @@ estimated_params;
rhog, uniform_pdf,,,0,1;
thetabig, gamma_pdf, 3, 1.42, 1, ;
//$Parameter for the hybrid Phillips curve
//omega, uniform_pdf,,,0,1;
// Parameter for the hybrid Phillips curve
// omega, uniform_pdf,,,0,1;
dsge_prior_weight, uniform_pdf,,,0,1.9;
end;
//$ Declaration of the observed endogenous variables. Note that they are the variables of the VAR (4 by default) and that we must
//$ have as many observed variables as exogenous variables.
/* Declaration of the observed endogenous variables. Note that they are the variables of the VAR (with 4 lags by default) and that we must
** have as many observed variables as exogenous variables.
*/
varobs pie r rw y;
options_.gradient_method = 3;
/* REMARK 1.
** The dsge_var option triggers the estimation of a DSGE-VAR model instead of the plain DSGE model. The weight of the DSGE prior, dsge_prior_weight,
** is estimated. The prior of this parameter is defined in the estimated_params block.
**
** REMARK 2.
** The option bayesian_irf triggers the computation of the DSGE-VAR and DSGE posterior distribution of the IRFs.
** The Dashed lines are the first, fifth (ie the median) and ninth posterior deciles of the DSGE-VAR's IRFs, the bold dark curve is the
** posterior median of the DSGE's IRfs and the shaded surface covers the space between the first and ninth posterior deciles of the DSGE's IRFs.
*/
//$ The option bayesian_irf triggers the computation of the DSGE-VAR and DSGE posterior distribution of the IRFs.
//$ The Dashed lines are the first, fifth (ie the median) and ninth posterior deciles of the DSGE-VAR's IRFs, the bold dark curve is the
//$ posterior median of the DSGE's IRfs and the shaded surface covers the space between the first and ninth posterior deciles of the DSGE's IRFs.
estimation(datafile=datarabanal_hybrid,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var,mode_compute=4,mh_replic=2000,bayesian_irf);
estimation(datafile=datarabanal_hybrid,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var,mode_compute=4,optim=('NumgradAlgorithm',3),mh_replic=2000,bayesian_irf);