Bayesian Model Averaging in the Instrumental Variable Regression Model*

Gary Koop, Roberto Leon-Gonzalez and Rodney Strachan

No 1112, Working Papers from University of Strathclyde Business School, Department of Economics

Abstract: This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very ?exible and can be easily adapted to analyze any of the di¤erent priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Keywords: Bayesian; endogeneity; simultaneous equations; reversible jump Markov chain Monte Carlo. (search for similar items in EconPapers)
JEL-codes: C30 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2011-04
New Economics Papers: this item is included in nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Bayesian model averaging in the instrumental variable regression model (2012)
Working Paper: Bayesian Model Averaging in the Instrumental Variable Regression Model (2012)
Working Paper: Bayesian Model Averaging in the Instrumental Variable Regression Model (2011)
Working Paper: Bayesian Model Averaging in the Instrumental Variable Regression Model (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:str:wpaper:1112

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