Computational Efficiency in Bayesian Model and Variable Selection
This paper is concerned with the efficient implementation of Bayesian model averaging (BMA) and Bayesian variable selection, when the number of candidate variables and models is large, and estimation of posterior model probabilities must be based on a subset of the models. Efficient implementation is concerned with two issues, the efficiency of the MCMC algorithm itself and efficient computation of the quantities needed to obtain a draw from the MCMC algorithm. For the first aspect, it is desirable that the chain moves well and quickly through the model space and takes draws from regions with high probabilities. In this context there is a natural trade-off between local moves, which make use of the current parameter values to propose plausible values for model parameters, and more global transitions, which potentially allow exploration of the distribution of interest in fewer steps, but where each step is more computationally intensive. We assess the convergence properties of simple samplers based on local moves and some recently proposed algorithms intended to improve on the basic samplers. For the second aspect, efficient computation within the sampler, we focus on the important case of linear models where the computations essentially reduce to least squares calculations. When the chain makes local moves, adding or dropping a variable, substantial gains in efficiency can be made by updating the previous least squares solution.
|Date of creation:||May 2007|
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- Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December.
- Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998.
"Benchmark priors for Bayesian model averaging,"
ESE Discussion Papers
26, Edinburgh School of Economics, University of Edinburgh.
- Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998. "Benchmark priors for Bayesian model averaging," ESE Discussion Papers 66, Edinburgh School of Economics, University of Edinburgh.
- Carmen Fernandez & Eduardo Ley & Mark F.J. Steel, 1998. "Benchmark Priors for Bayesian Model Averaging," Econometrics 9804001, EconWPA, revised 31 Jul 1999.
- Carmen Fernández & Eduardo Ley & Mark F. J. Steel, . "Benchmark priors for Bayesian Model averaging," Working Papers 98-06, FEDEA.
- Smith, M. & Kohn, R., .
"Nonparametric Regression using Bayesian Variable Selection,"
Statistics Working Paper
_009, Australian Graduate School of Management.
- Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
- Jacobson, Tor & Karlsson, Sune, 2002.
"Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach,"
Working Paper Series
138, Sveriges Riksbank (Central Bank of Sweden).
- Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
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