GRIPS 政策研究センター Policy Research Center

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2011/1/24 Report No:10-32

Bayesian Model Averaging in the Instrumental Variable Regression Model

著者
  • Gary KoopRimini Center for Economic Analysis
  • Roberto Leon Gonzalez政策研究大学院大学
  • Rodney StrachanRimini Center for Economic Analysis
分野 経済学
言語 英語
要旨

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.

キーワード Bayesian, endogeneity, simultaneous equations, reversible jump Markov chain Monte Carlo.
添付ファイル 10-32.pdf