L. Bauwens (Luc)
http://repub.eur.nl/ppl/3229/
List of Publicationsenhttp://repub.eur.nl/logo.jpg
http://repub.eur.nl/
RePub, Erasmus University RepositoryAdaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods
http://repub.eur.nl/pub/11191/
Wed, 01 Dec 2004 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with non-elliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformation a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. The ARDS algorithms are illustrated on a regression model with scale contamination and a mixture model for economic growth of the USA.Editorâ€™s introduction to recent advances in Bayesian econometrics
http://repub.eur.nl/pub/11375/
Thu, 01 Jan 2004 00:00:01 GMT<div>L. Bauwens</div><div>M. Lubrano</div><div>H.K. van Dijk</div>
Explaining Adaptive Radial-Based Direction Sampling
http://repub.eur.nl/pub/1045/
Thu, 07 Aug 2003 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
In this short paper we summarize the computational steps of Adaptive Radial-Based Direction Sampling (ARDS), which can be used for Bayesian analysis of ill behaved target densities. We consider one simulation experiment in order to illustrate the good performance of ARDS relative to the independence chain MH algorithm and importance sampling.Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods
http://repub.eur.nl/pub/1722/
Wed, 06 Aug 2003 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with nonelliptical, possibly, multimodal target distributions.
A key step is a radial-based transformation to directions and distances. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution by means of the numerical inverse transformation method. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the ARDS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. The empirical examples include a regression model with scale contamination and a mixture model for economic growth of the USA.Adaptive polar sampling, a class of flexibel and robust Monte Carlo integration methods
http://repub.eur.nl/pub/555/
Tue, 17 Sep 2002 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
Adaptive Polar Sampling (APS) algorithms are proposed for Bayesian analysis of models with
nonelliptical, possibly, multimodal posterior distributions. A location-scale transformation
and a transformation to polar coordinates are used. After the transformation to polar
coordinates, a Metropolis-Hastings method or, alternatively, an importance sampling
method is applied to sample directions and, conditionally on these, distances are
generated by inverting the cumulative distribution function. A sequential procedure is
applied to update the initial location and scaling matrix in order to sample directions
in an efficient way. Tested on a set of canonical mixture models that feature multimodality,
strong correlation, and skewness, the APS algorithms compare favourably with the standard
Metropolis-Hastings and importance samplers in terms of flexibility and robustness. APS is
applied to several econometric and statistical examples. The empirical results for a
regression model with scale contamination, an ARMA-GARCH-Student t model with near
cancellation of roots and heavy tails, a mixture model for economic growth, and a
nonlinear threshold model for industrial production growth confirm the practical
flexibility and robustness of APS.Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk
http://repub.eur.nl/pub/7712/
Thu, 21 Oct 1999 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div>
Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings algorithm is applied to sample directions and, conditionally on these, distances are generated by inverting the CDF. A sequential procedure is applied to update the location and scale.
Tested on a set of canonical models that feature near non-identifiability, strong correlation, and bimodality, APS compares favourably with the standard Metropolis-Hastings sampler in terms of parsimony and robustness. APS is applied within a Bayesian analysis of a GARCH-mixture model which is used for the evaluation of the Value-at-Risk of the return of the Dow Jones stock index.Adaptive polar sampling: a new MC technique for the analysis of ill behaved surfaces
http://repub.eur.nl/pub/1550/
Thu, 02 Jul 1998 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div>
Adaptive Polar Sampling is proposed as an algorithm where random drawings are directly generated from the target function (posterior) in
all-but-one directions of the parameter space. The method is based on the mixed integration technique of Van Dijk, Kloek & Boender (1985) but
extends this one by replacing the one-dimensional quadrature step by Monte Carlo simulation from this one-dimensional distribution function.
The method is particularly suited for the analysis of ill-behaved surfaces. An illustrative example shows the feasibility of the
algorithm.Bayes, Bernoullis, and Basel, Editorâ€™s introduction
http://repub.eur.nl/pub/11316/
Mon, 01 Jan 1996 00:00:01 GMT<div>L. Bauwens</div><div>H.K. van Dijk</div>
Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods
http://repub.eur.nl/pub/11238/
Fri, 01 Jan 1988 00:00:01 GMT<div>A. Zellner</div><div>L. Bauwens</div><div>H.K. van Dijk</div>
Bayesian procedures for specification analysis or diagnostic checking of modeling assumptions for structural equations of econometric models are developed and applied using Monte Carlo numerical methods. Checks on the validity of identifying restrictions, exogeneity assumptions and other specifying assumptions are performed using posterior distributions for discrepancy vectors and functions representing departures from specifying assumptions. Several mappings or functions of reduced form coefficients are defined and their posterior distributions are computed. A restricted reduced form approach is used to compute posterior distributions for structural parameters. These procedures are applied in analyses of two econometric models.