Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86808
Authors: 
Ardia, David
Basturk, Nalan
Hoogerheide, Lennart
van Dijk, Herman K.
Year of Publication: 
2010
Series/Report no.: 
Tinbergen Institute Discussion Paper 10-059/4
Abstract: 
Strategic choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. A comparative analysis is presented of possible advantages and limitations of different simulation techniques; of possible choices of candidate distributions and choices of target or warped target distributions; and finally of numerical standard errors. The importance of a robust and flexible estimation strategy is demonstrated where the complete posterior distribution is explored. Given an appropriately yet quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the marginal likelihood (and a reliable and easily computed corresponding numerical standard error) in the cases investigated in this paper, which include a non-linear regression model and a mixture GARCH model. Warping the posterior density can lead to a further gain in efficiency, but it is more important that the posterior kernel is appropriately wrapped by the candidate distribution than that is warped.
Subjects: 
marginal likelihood
Bayes factor
importance sampling
bridge sampling
adaptive mixture of Student-t distributions
JEL: 
C11
C15
C52
Document Type: 
Working Paper

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