Bayesian model averaging in the instrumental variable regression model

Gary Koop, Roberto Leon-Gonzalez, Rodney Strachan

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)
50 Downloads (Pure)


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 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.
Original languageEnglish
Pages (from-to)237–250
Number of pages14
JournalJournal of Econometrics
Issue number2
Publication statusPublished - Dec 2012


  • Bayesian
  • endogeneity
  • simultaneous equations
  • reversible jump
  • Markov chain


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