Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach
Article first published online: 1 FEB 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 27, Issue 1, pages 108–128, January/February 2012
How to Cite
Kraay, A. (2012), Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach. J. Appl. Econ., 27: 108–128. doi: 10.1002/jae.1148
- Issue published online: 1 FEB 2010
- Article first published online: 1 FEB 2010
- Manuscript Revised: 5 AUG 2009
- Manuscript Received: 9 FEB 2009
The identification of structural parameters in the linear instrumental variables (IV) model is typically achieved by imposing the prior identifying assumption that the error term in the structural equation of interest is orthogonal to the instruments. Since this exclusion restriction is fundamentally untestable, there are often legitimate doubts about the extent to which the exclusion restriction holds. In this paper I illustrate the effects of such prior uncertainty about the validity of the exclusion restriction on inferences based on linear IV models. Using a Bayesian approach, I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. I illustrate these points using several prominent recent empirical papers that use linear IV models. An accompanying electronic table allows users to readily explore the robustness of inferences to uncertainty about the exclusion restriction in their particular applications. Copyright © 2010 John Wiley & Sons, Ltd.