Computational Efficiency in Bayesian Model and Variable Selection
AbstractLarge scale Bayesian model averaging and variable selection exercises present, despite the great increase in desktop computing power, considerable computational challenges. Due to the large scale it is impossible to evaluate all possible models and estimates of posterior probabilities are instead obtained from stochastic (MCMC) schemes designed to converge on the posterior distribution over the model space. While this frees us from the requirement of evaluating all possible models the computational effort is still substantial and efficient implementation is vital. 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. We evaluate several different MCMC algorithms and find that relatively simple algorithms with local moves perform competitively except possibly when the data is highly collinear. 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. Least squares solvers that update a previous model estimate are appealing when the MCMC algorithm makes local moves and we find that the Cholesky update is both fast and accurate.
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Bibliographic InfoPaper provided by Örebro University, School of Business in its series Working Papers with number 2007:4.
Length: 41 pages
Date of creation: 10 Sep 2007
Date of revision:
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Web page: http://www.oru.se/Institutioner/Handelshogskolan-vid-Orebro-universitet/
More information through EDIRC
Bayesian Model Averaging; Sweep operator; Cholesky decomposition; QR decomposition; Swendsen-Wang algorithm;
Other versions of this item:
- Jana Eklund & Sune Karlsson, 2007. "Computational Efficiency in Bayesian Model and Variable Selection," Economics wp35, Department of Economics, Central bank of Iceland.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-11-03 (All new papers)
- NEP-CMP-2007-11-03 (Computational Economics)
- NEP-ECM-2007-11-03 (Econometrics)
- NEP-LAB-2007-11-03 (Labour Economics)
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.:
- 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 & E Ley & Mark F J Steel, 2004.
"Benchmark priors for Bayesian models averaging,"
ESE Discussion Papers
66, Edinburgh School of Economics, University of Edinburgh.
- Smith, Michael & Kohn, Robert, 1996.
"Nonparametric regression using Bayesian variable selection,"
Journal of Econometrics,
Elsevier, vol. 75(2), pages 317-343, December.
- Smith, M. & Kohn, R., . "Nonparametric Regression using Bayesian Variable Selection," Statistics Working Paper _009, Australian Graduate School of Management.
- 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.
- Jesus Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2008.
"The Determinants of Economic Growth in European Regions,"
2008-26, Faculty of Economics and Statistics, University of Innsbruck.
- Jes�s Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2014. "The Determinants of Economic Growth in European Regions," Regional Studies, Taylor & Francis Journals, vol. 48(1), pages 44-67, January.
- Jesus Crespo-Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2009. "The Determinants of Economic Growth in European Regions," wiiw Working Papers 57, The Vienna Institute for International Economic Studies, wiiw.
- Jesús Crespo-Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2009. "The Determinants of Economic Growth in European Regions," CESifo Working Paper Series 2519, CESifo Group Munich.
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