Bayesian 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.
|Place of Publication||TILBURG|
|Number of pages||29|
|Publication status||Published - 2011|
|Name||CentER Discussion Paper|
- Model averaging
- Bayesian analysis
- Subbotin prior