Bayesian Model Averaging in the Instrumental Variable Regression Model

Gary Koop, Robert Leon Gonzalez and Rodney Strachan
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Robert Leon Gonzalez: National Graduate Institute for Policy Studies
Authors registered in the RePEc Author Service: Roberto Leon-Gonzalez

No 10-32, GRIPS Discussion Papers from National Graduate Institute for Policy Studies

Abstract: This paper considers the instrumental variable regression model when there is uncertainly about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainly 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 flexible and can be easily adapted to analyze any of the different 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)
Pages: 48 pages
Date: 2011-03
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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:ngi:dpaper:10-32

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