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Document inefficiency factor computation

time-shift
Johannes Pfeifer 2016-05-19 16:33:06 +02:00
parent 048fbcc698
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@ -4759,7 +4759,11 @@ marginal log data density
posterior mean and highest posterior density interval (shortest credible set) from posterior simulation
@item
Metropolis-Hastings convergence graphs that still need to be documented
convergence diagnostic table when only one MCM chain is used or Metropolis-Hastings convergence graphs documented in @cite{Pfeifer (2014)}
in case of multiple MCM chains
@item
table with numerical inefficiency factors of the MCMC
@item
graphs with prior, posterior, and mode
@ -4811,7 +4815,7 @@ set_dynare_seed('clock');
@algorithmshead
The Monte Carlo Markov Chain (MCMC) diagnostics are generated by the
The Monte Carlo Markov Chain (MCMC) diagnostics are generated by the
estimation command if @ref{mh_replic} is larger than 2000 and if option
@ref{nodiagnostic} is not used. If @ref{mh_nblocks} is equal to one, the
convergence diagnostics of @cite{Geweke (1992,1999)} is computed. It
@ -4834,6 +4838,11 @@ used to aggregate the parameters into a scalar statistic whose
convergence is then checked using the @cite{Brooks and Gelman (1998)}
univariate convergence diagnostic.
The inefficiency factors are computed as in @cite{Giordano et al. (2011)} based on
Parzen windows as in e.g. @cite{Andrews (1991)}.
based on Parzen
@optionshead
@table @code
@ -13365,6 +13374,10 @@ Cycles: The Cycle is the Trend,'' @i{NBER Working Paper}, 10734
@item
Andreasen, Martin M., Jesús Fernández-Villaverde, and Juan Rubio-Ramírez (2013): ``The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications,'' @i{NBER Working Paper}, 18983
@item
Andrews, Donald W.K (1991): ``Heteroskedasticity and autocorrelation consistent covariance matrix estimation'',
@i{Econometrica}, 59(3), 817--858
@item
Backus, David K., Patrick J. Kehoe, and Finn E. Kydland (1992):
``International Real Business Cycles,'' @i{Journal of Political
@ -13460,6 +13473,11 @@ International Meeting on Bayesian Statistics, pp. 169--194, Oxford University Pr
Geweke, John (1999): ``Using simulation methods for Bayesian econometric models:
Inference, development and communication,'' @i{Econometric Reviews}, 18(1), 1--73
@item
Giordani, Paolo, Michael Pitt, and Robert Kohn (2011): ``Bayesian Inference for Time Series State Space Models''
in: @i{The Oxford Handbook of Bayesian Econometrics}, ed. by John Geweke, Gary Koop, and Herman van Dijk,
Oxford University Press, 61--124
@item
Goffe, William L., Gary D. Ferrier, and John Rogers (1994): ``Global Optimization
of Statistical Functions with Simulated Annealing,'' @i{Journal of Econometrics}, 60(1/2),
@ -13552,6 +13570,9 @@ of DSGE models''
@item
Pfeifer, Johannes (2013): ``A Guide to Specifying Observation Equations for the Estimation of DSGE Models''
@item
Pfeifer, Johannes (2014): ``An Introduction to Graphs in Dynare''
@item
Rabanal, Pau and Juan Rubio-Ramirez (2003): ``Comparing New Keynesian
Models of the Business Cycle: A Bayesian Approach,'' Federal Reserve