客員研究員

Jan 24, 2011 Report No:10-32

Bayesian Model Averaging in the Instrumental Variable Regression Model

Author
  • Gary KoopRimini Center for Economic Analysis
  • Roberto Leon GonzalezGRIPS
  • Rodney StrachanRimini Center for Economic Analysis
Field Economics
Language English
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.
attachment 10-32.pdf