On the Choice of Prior in Bayesian Model Averaging
AbstractBayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable’ prior as one which leads to a continuous and suitable analog to the pretest estimator. The normal prior, used in standard Bayesian model averaging, is shown to be unsuitable. The Laplace (or lasso) prior is almost suitable. A suitable prior (the Subbotin prior) is proposed and its properties are investigated.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2011-003.
Date of creation: 2011
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Model averaging; Bayesian analysis; Subbotin prior;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- Giuseppe De Luca & Jan R. Magnus, 2011.
"Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues,"
StataCorp LP, vol. 11(4), pages 518-544, December.
- De Luca, G. & Magnus, J.R., 2011. "Bayesian Model Averaging and Weighted Average Least Squares: Equivariance, Stability, and Numerical Issues," Discussion Paper 2011-082, Tilburg University, Center for Economic Research.
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