A Comparison of Marginal Likelihood Computation Methods

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A Comparison of Marginal Likelihood Computation Methods

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Charles S. Bos () (Vrije Universiteit Amsterdam)

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Charles S. Bos

Abstract

In a Bayesian analysis, different models can be compared on the basis of the expected or marginal likelihood they attain. Many methods have been devised to compute the marginal likelihood, but simplicity is not the strongest point of most methods. At the same time, the precision of methods is often questionable. In this paper several methods are presented in a common framework. The explanation of the differences is followed by an application, in which the precision of the methods is tested on a simple regression model where a comparison with analytical results is possible.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 02-084/4.

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Date of creation: 17 Sep 2002
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Handle: RePEc:dgr:uvatin:20020084

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

This paper has been announced in the following NEP Reports:

NEP-ALL-2002-12-02 (All new papers)
NEP-CMP-2002-12-02 (Computational Economics)
NEP-ECM-2002-12-10 (Econometrics)

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July. [Downloadable!] (restricted)
Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January. [Downloadable!] (restricted)
G. Koop & H.K. van Dijk, 1999. "Testing for integration using evolving trend and seasonal models A Bayesian approach," Econometric Institute Report 163, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
Other versions:
- Gary Koop & Herman K. van Dijk, 1999. "Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach," Tinbergen Institute Discussion Papers 99-072/4, Tinbergen Institute. [Downloadable!]
- Gary Koop & Herman K. van Dijk & Henk Hoek, 1997. "Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach," Tinbergen Institute Discussion Papers 97-078/4, Tinbergen Institute. [Downloadable!]
- Van Dijk, H.K. & Koop, G., 1999. "Testing for Integration Using Evolving Trend and Seasonals Models : A Bayesian Approach," Papers 9934/a, Erasmus University of Rotterdam - Econometric Institute.
- Koop, Gary & Dijk, Herman K. Van, 2000. "Testing for integration using evolving trend and seasonals models: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 97(2), pages 261-291, August. [Downloadable!] (restricted)
Koop, Gary & Potter, Simon M., 1998. "Bayes factors and nonlinearity: Evidence from economic time series1," Journal of Econometrics, Elsevier, vol. 88(2), pages 251-281, November. [Downloadable!] (restricted)

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

Pierangelo De Pace, 2005. "Grid-Bootstrap Methods vs. Bayesian Analysis. Testing for Structural Breaks in the Conditional Variance of Nominal Interest Rate Spreads - Four Cases in Europe," Econometrics 0509011, EconWPA, revised 07 Sep 2005. [Downloadable!]
L. Bauwens & C.S. Bos & H.K. Van Dijk & R.D. Van Oest, 2002. "Adaptive polar sampling, a class of flexibel and robust Monte Carlo integration methods," Econometric Institute Report 278, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
Charles S. Bos & Neil Shephard, 2004. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Economics Papers 2004-W02, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
Other versions:
- Charles S. Bos & Neil Shephard, 2004. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space form," Tinbergen Institute Discussion Papers 04-015/4, Tinbergen Institute. [Downloadable!]

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