Commit 0845fb37 authored by Stéphane Adjemian's avatar Stéphane Adjemian
Browse files

Added a description of the MCMC diagnostics with a reference to Brooks and Gelman (1998).

parent 5c936cc0
......@@ -4182,6 +4182,24 @@ graphs with prior, posterior and mode
graphs of smoothed shocks, smoothed observation errors, smoothed and historical variables
@end itemize
The Monte Carlo Markov Chain (MCMC) univariate diagnostics are generated
by the estimation command if @ref{mh_nblocks} is larger than 1, if
@ref{mh_replic} is larger than 2000, and if option @ref{nodiagnostic} is
not used. As described in section 3 of @cite{Brooks and Gelman (1998)}
the convergence diagnostics are based on comparing pooled and within
MCMC moments (Dynare displays the second and third order moments, and
the length of the Highest Probability Density interval covering 80% of
the posterior distribution). Due to computational reasons, the
multivariate convergence diagnostic does not follow of @cite{Brooks and
Gelman (1998)} strictly, but rather applies their idea for univariate
convergence diagnostics to the range of the posterior likelihood
function instead of the individual parameters. The posterior kernel is
used to aggregate the parameters into a scalar statistic whose
convergence is then checked using the @cite{Brooks and Gelman (1998)}
univariate convergence diagnostic.
@table @code
......@@ -4308,7 +4326,7 @@ the total number of Metropolis draws available. Default:
@item mh_nblocks = @var{INTEGER}
Number of parallel chains for Metropolis-Hastings algorithm. Default:
@anchor{mh_nblocks} Number of parallel chains for Metropolis-Hastings algorithm. Default:
@item mh_drop = @var{DOUBLE}
......@@ -4445,7 +4463,7 @@ options. Default:
@item nodiagnostic
Doesn't compute the convergence diagnostics for
@anchor{nodiagnostic} Does not compute the convergence diagnostics for
Metropolis-Hastings. Default: diagnostics are computed and displayed
@item bayesian_irf
......@@ -8482,6 +8500,11 @@ Boucekkine, Raouf (1995): ``An alternative methodology for solving
nonlinear forward-looking models,'' @i{Journal of Economic Dynamics
and Control}, 19, 711--734
Brooks, Stephen P., and Andrew Gelman (1998): ``General methods for
monitoring convergence of iterative simulations,'' @i{Journal of
computational and graphical statistics}, 7, pp. 434--455
Collard, Fabrice (2001): ``Stochastic simulations with Dynare: A practical guide''
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