Variable Selection in Bayesian Models: Using Parameter Estimation and Non Parameter Estimation Methods

Gail Blattenberger, Richard Fowles and Peter D. Loeb

A chapter in Bayesian Model Comparison, 2014, vol. 34, pp 249-278 from Emerald Group Publishing Limited

Abstract: This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.

Keywords: Bayesian variable selection; Extreme Bounds Analysis; stochastic search model selection; Bayesian tree models; motor vehicle fatality rates; Bayesian Model Averaging; C11; C14; L9 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320140000034011

DOI: 10.1108/S0731-905320140000034011

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