Merge pull request #842 from JohannesPfeifer/cosmetics
Cosmetical changes and additional documentationtime-shift
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@ -5318,7 +5318,15 @@ missing observations.
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@item endogenous_prior
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@item endogenous_prior
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Use endogenous priors as in @cite{Christiano, Trabandt and Walentin
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Use endogenous priors as in @cite{Christiano, Trabandt and Walentin
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(2011)}.
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(2011)}.
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The procedure is motivated by sequential Bayesian learning. Starting from independent initial priors on the parameters,
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specified in the @code{estimated_params}-block, the standard deviations observed in a "pre-sample",
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taken to be the actual sample, are used to update the initial priors. Thus, the product of the initial
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priors and the pre-sample likelihood of the standard deviations of the observables is used as the new prior
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(for more information, see the technical appendix of @cite{Christiano, Trabandt and Walentin (2011)}).
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This procedure helps in cases where the regular posterior estimates, which minimize in-sample forecast
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errors, result in a large overprediction
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of model variable variances (a statistic that is not explicitly targeted, but often of particular interest to researchers).
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@item use_univariate_filters_if_singularity_is_detected = @var{INTEGER}
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@item use_univariate_filters_if_singularity_is_detected = @var{INTEGER}
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@anchor{use_univariate_filters_if_singularity_is_detected}
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@anchor{use_univariate_filters_if_singularity_is_detected}
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@ -4,7 +4,7 @@ function [dr,info,M,options,oo] = resol(check_flag,M,options,oo)
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%! @deftypefn {Function File} {[@var{dr},@var{info},@var{M},@var{options},@var{oo}] =} resol (@var{check_flag},@var{M},@var{options},@var{oo})
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%! @deftypefn {Function File} {[@var{dr},@var{info},@var{M},@var{options},@var{oo}] =} resol (@var{check_flag},@var{M},@var{options},@var{oo})
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%! @anchor{resol}
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%! @anchor{resol}
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%! @sp 1
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%! @sp 1
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%! Computes first and second order reduced form of the DSGE model.
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%! Computes the perturbation-based decisions rules of the DSGE model (orders 1 to 3).
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%! @sp 2
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%! @sp 2
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%! @strong{Inputs}
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%! @strong{Inputs}
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%! @sp 1
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%! @sp 1
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@ -1,7 +1,7 @@
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function [dr,info] = stochastic_solvers(dr,task,M_,options_,oo_)
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function [dr,info] = stochastic_solvers(dr,task,M_,options_,oo_)
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% function [dr,info,M_,options_,oo_] = stochastic_solvers(dr,task,M_,options_,oo_)
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% function [dr,info,M_,options_,oo_] = stochastic_solvers(dr,task,M_,options_,oo_)
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% computes the reduced form solution of a rational expectation model (first or second order
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% computes the reduced form solution of a rational expectations model (first, second or third
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% approximation of the stochastic model around the deterministic steady state).
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% order approximation of the stochastic model around the deterministic steady state).
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%
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%
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% INPUTS
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% INPUTS
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% dr [matlab structure] Decision rules for stochastic simulations.
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% dr [matlab structure] Decision rules for stochastic simulations.
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