Variable selection in Bayesian Models: Using parameter estimation and non parameter estimation methods

Gail Blattenberger, Richard Fowles, Peter D. Loeb

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)249-278
Number of pages30
JournalAdvances in Econometrics
Volume34
DOIs
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • Bayesian model averaging
  • Bayesian tree models
  • Bayesian variable selection
  • Extreme bounds analysis
  • Motor vehicle fatality rates
  • Stochastic search model selection

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