Merge pull request #1361 from JohannesPfeifer/endogenous_prior_restrictions
Document irf_calibration and moment_calibration in the context of est…time-shift
commit
420fb3896b
|
@ -5092,8 +5092,6 @@ univariate convergence diagnostic.
|
||||||
The inefficiency factors are computed as in @cite{Giordano et al. (2011)} based on
|
The inefficiency factors are computed as in @cite{Giordano et al. (2011)} based on
|
||||||
Parzen windows as in e.g. @cite{Andrews (1991)}.
|
Parzen windows as in e.g. @cite{Andrews (1991)}.
|
||||||
|
|
||||||
based on Parzen
|
|
||||||
|
|
||||||
@optionshead
|
@optionshead
|
||||||
|
|
||||||
@table @code
|
@table @code
|
||||||
|
@ -6324,6 +6322,16 @@ all parameters. Note that if @code{mode_compute=6} is used or the @code{posterio
|
||||||
called @code{scale_file} is specified, the values set in @code{estimated_params}
|
called @code{scale_file} is specified, the values set in @code{estimated_params}
|
||||||
will be overwritten.
|
will be overwritten.
|
||||||
|
|
||||||
|
@customhead{``Endogenous'' prior restrictions}
|
||||||
|
|
||||||
|
It is also possible to impose implicit ``endogenous'' priors about IRFs and moments on the model during
|
||||||
|
estimation. For example, one can specify that all valid parameter draws for the model must generate fiscal multipliers that are
|
||||||
|
bigger than 1 by specifying how the IRF to a government spending shock must look like. The prior restrictions can be imposed
|
||||||
|
via @code{irf_calibration} and @code{moment_calibration} blocks (@pxref{IRF/Moment calibration}). The way it works internally is that
|
||||||
|
any parameter draw that is inconsistent with the ``calibration'' provided in these blocks is discarded, i.e. assigned a prior density of 0.
|
||||||
|
When specifying these blocks, it is important to keep in mind that one won't be able to easily do @code{model_comparison} in this case,
|
||||||
|
because the prior density will not integrate to 1.
|
||||||
|
|
||||||
@outputhead
|
@outputhead
|
||||||
|
|
||||||
@vindex M_.params
|
@vindex M_.params
|
||||||
|
@ -8449,6 +8457,12 @@ Maximum number of lags for moments in identification analysis. Default: @code{1}
|
||||||
@node IRF/Moment calibration
|
@node IRF/Moment calibration
|
||||||
@subsection IRF/Moment calibration
|
@subsection IRF/Moment calibration
|
||||||
|
|
||||||
|
The @code{irf_calibration} and @code{moment_calibration} blocks allow imposing implicit ``endogenous'' priors
|
||||||
|
about IRFs and moments on the model. The way it works internally is that
|
||||||
|
any parameter draw that is inconsistent with the ``calibration'' provided in these blocks is discarded, i.e. assigned a prior density of 0.
|
||||||
|
In the context of @code{dynare_sensitivity}, these restrictions allow tracing out which parameters are driving the model to
|
||||||
|
satisfy or violate the given restrictions.
|
||||||
|
|
||||||
IRF and moment calibration can be defined in @code{irf_calibration} and @code{moment_calibration} blocks:
|
IRF and moment calibration can be defined in @code{irf_calibration} and @code{moment_calibration} blocks:
|
||||||
|
|
||||||
@deffn Block irf_calibration ;
|
@deffn Block irf_calibration ;
|
||||||
|
|
|
@ -32,6 +32,7 @@ MODFILES = \
|
||||||
estimation/fs2000_calibrated_covariance.mod \
|
estimation/fs2000_calibrated_covariance.mod \
|
||||||
estimation/fs2000_model_comparison.mod \
|
estimation/fs2000_model_comparison.mod \
|
||||||
estimation/fs2000_fast.mod \
|
estimation/fs2000_fast.mod \
|
||||||
|
estimation/ls2003_endog_prior_restrict_estimation.mod \
|
||||||
estimation/independent_mh/fs2000_independent_mh.mod \
|
estimation/independent_mh/fs2000_independent_mh.mod \
|
||||||
estimation/MH_recover/fs2000_recover.mod \
|
estimation/MH_recover/fs2000_recover.mod \
|
||||||
estimation/MH_recover/fs2000_recover_2.mod \
|
estimation/MH_recover/fs2000_recover_2.mod \
|
||||||
|
|
|
@ -0,0 +1,85 @@
|
||||||
|
//conducts estimation with an "endogenous" prior restriction specified via irf_calibration and moment_calibration
|
||||||
|
|
||||||
|
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
|
||||||
|
varexo e_R e_q e_ys e_pies e_A;
|
||||||
|
|
||||||
|
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
|
||||||
|
|
||||||
|
psi1 = 1.54;
|
||||||
|
psi2 = 0.25;
|
||||||
|
psi3 = 0.25;
|
||||||
|
rho_R = 0.5;
|
||||||
|
alpha = 0.3;
|
||||||
|
rr = 2.51;
|
||||||
|
k = 0.5;
|
||||||
|
tau = 0.5;
|
||||||
|
rho_q = 0.4;
|
||||||
|
rho_A = 0.2;
|
||||||
|
rho_ys = 0.9;
|
||||||
|
rho_pies = 0.7;
|
||||||
|
|
||||||
|
|
||||||
|
model(linear);
|
||||||
|
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
|
||||||
|
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
|
||||||
|
pie = de+(1-alpha)*dq+pie_s;
|
||||||
|
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
|
||||||
|
dq = rho_q*dq(-1)+e_q;
|
||||||
|
y_s = rho_ys*y_s(-1)+e_ys;
|
||||||
|
pie_s = rho_pies*pie_s(-1)+e_pies;
|
||||||
|
A = rho_A*A(-1)+e_A;
|
||||||
|
y_obs = y-y(-1)+A;
|
||||||
|
pie_obs = 4*pie;
|
||||||
|
R_obs = 4*R;
|
||||||
|
end;
|
||||||
|
|
||||||
|
shocks;
|
||||||
|
var e_R = 1.25^2;
|
||||||
|
var e_q = 2.5^2;
|
||||||
|
var e_A = 1.89;
|
||||||
|
var e_ys = 1.89;
|
||||||
|
var e_pies = 1.89;
|
||||||
|
end;
|
||||||
|
|
||||||
|
varobs y_obs R_obs pie_obs dq de;
|
||||||
|
|
||||||
|
estimated_params;
|
||||||
|
psi1 , gamma_pdf,1.5,0.5;
|
||||||
|
psi2 , gamma_pdf,0.25,0.125;
|
||||||
|
psi3 , gamma_pdf,0.25,0.125;
|
||||||
|
rho_R ,beta_pdf,0.5,0.2;
|
||||||
|
alpha ,beta_pdf,0.3,0.1;
|
||||||
|
rr ,gamma_pdf,2.5,1;
|
||||||
|
k , gamma_pdf,0.5,0.25;
|
||||||
|
tau ,gamma_pdf,0.5,0.2;
|
||||||
|
rho_q ,beta_pdf,0.4,0.2;
|
||||||
|
rho_A ,beta_pdf,0.5,0.2;
|
||||||
|
rho_ys ,beta_pdf,0.8,0.1;
|
||||||
|
rho_pies,beta_pdf,0.7,0.15;
|
||||||
|
stderr e_R,inv_gamma_pdf,(1.2533/3),(0.6551/10);
|
||||||
|
stderr e_q,inv_gamma_pdf,(2.5066/3),(1.3103/10);
|
||||||
|
stderr e_A,inv_gamma_pdf,(1.2533/3),(0.6551/10);
|
||||||
|
stderr e_ys,inv_gamma_pdf,(1.2533/3),(0.6551/10);
|
||||||
|
stderr e_pies,inv_gamma_pdf,(1.88/3),(0.9827/10);
|
||||||
|
end;
|
||||||
|
|
||||||
|
// endogenous prior restrictions
|
||||||
|
irf_calibration(relative_irf);
|
||||||
|
y(1:4), e_ys, [ -50 50]; //[first year response]
|
||||||
|
@#for ilag in 21:40
|
||||||
|
R_obs(@{ilag}), e_ys, [0 6]; //[response after 4th year to 10th year]
|
||||||
|
@#endfor
|
||||||
|
end;
|
||||||
|
|
||||||
|
moment_calibration;
|
||||||
|
y_obs,y_obs(-(1:4)), +; //[first year acf]
|
||||||
|
@#for ilag in -2:2
|
||||||
|
y_obs,R_obs(@{ilag}), -; //[ccf]
|
||||||
|
@#endfor
|
||||||
|
@#for ilag in -4:4
|
||||||
|
y_obs,pie_obs(@{ilag}), -; //[ccf]
|
||||||
|
@#endfor
|
||||||
|
end;
|
||||||
|
|
||||||
|
estimation(datafile='../gsa/data_ca1.m',mode_check,first_obs=8,nobs=79,mh_nblocks=1,
|
||||||
|
prefilter=1,mh_jscale=0.0005,mh_replic=5000, mode_compute=4, mh_drop=0.6, bayesian_irf,mcmc_jumping_covariance='identity_matrix') R_obs y;
|
Loading…
Reference in New Issue