From 823ed85e8dde99d7c896e0c6ddbf63aa7f519074 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Charybdis=29?= Date: Sun, 17 Nov 2013 12:55:56 +0100 Subject: [PATCH] Various changes in tests/dsge-var mod files. + Changed comments. + Use new Dynare's interface for specifying the version of numgrad. --- .../dsgevar_forward_calibrated_lambda.mod | 39 ++++++++-------- .../dsgevar_forward_estimated_lambda.mod | 44 +++++++++++-------- 2 files changed, 46 insertions(+), 37 deletions(-) diff --git a/tests/dsge-var/dsgevar_forward_calibrated_lambda.mod b/tests/dsge-var/dsgevar_forward_calibrated_lambda.mod index ce0e1dab9..abebf5d6f 100644 --- a/tests/dsge-var/dsgevar_forward_calibrated_lambda.mod +++ b/tests/dsge-var/dsgevar_forward_calibrated_lambda.mod @@ -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); diff --git a/tests/dsge-var/dsgevar_forward_estimated_lambda.mod b/tests/dsge-var/dsgevar_forward_estimated_lambda.mod index e867b0ea3..9fd000d43 100644 --- a/tests/dsge-var/dsgevar_forward_estimated_lambda.mod +++ b/tests/dsge-var/dsgevar_forward_estimated_lambda.mod @@ -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);