stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #842 from JohannesPfeifer/cosmetics 

Cosmetical changes and additional documentation
time-shift
Stéphane Adjemian 2015-02-21 15:42:51 +01:00
parent 7df3856cbf 8912ddf97c
commit 32c29fece5
3 changed files with 12 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
doc/dynare.texi
View File

@ -5318,7 +5318,15 @@ missing observations.
@item endogenous_prior
Use endogenous priors as in @cite{Christiano, Trabandt and Walentin
(2011)}.
(2011)}.
The procedure is motivated by sequential Bayesian learning. Starting from independent initial priors on the parameters,
specified in the @code{estimated_params}-block, the standard deviations observed in a "pre-sample",
taken to be the actual sample, are used to update the initial priors. Thus, the product of the initial
priors and the pre-sample likelihood of the standard deviations of the observables is used as the new prior
(for more information, see the technical appendix of @cite{Christiano, Trabandt and Walentin (2011)}).
This procedure helps in cases where the regular posterior estimates, which minimize in-sample forecast
errors, result in a large overprediction
of model variable variances (a statistic that is not explicitly targeted, but often of particular interest to researchers).
@item use_univariate_filters_if_singularity_is_detected = @var{INTEGER}
@anchor{use_univariate_filters_if_singularity_is_detected}

2
matlab/resol.m
View File

@ -4,7 +4,7 @@ function [dr,info,M,options,oo] = resol(check_flag,M,options,oo)
%! @deftypefn {Function File} {[@var{dr},@var{info},@var{M},@var{options},@var{oo}] =} resol (@var{check_flag},@var{M},@var{options},@var{oo})
%! @anchor{resol}
%! @sp 1
%! Computes first and second order reduced form of the DSGE model.
%! Computes the perturbation-based decisions rules of the DSGE model (orders 1 to 3).
%! @sp 2
%! @strong{Inputs}
%! @sp 1

4
matlab/stochastic_solvers.m
View File

@ -1,7 +1,7 @@
function [dr,info] = stochastic_solvers(dr,task,M_,options_,oo_)
% function [dr,info,M_,options_,oo_] = stochastic_solvers(dr,task,M_,options_,oo_)
% computes the reduced form solution of a rational expectation model (first or second order
% approximation of the stochastic model around the deterministic steady state).
% computes the reduced form solution of a rational expectations model (first, second or third
% order approximation of the stochastic model around the deterministic steady state).
%
% INPUTS
% dr [matlab structure] Decision rules for stochastic simulations.