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Instrumental variables regressions with honestly uncertain exclusion restrictions
 
Author:Kraay, Aart; Collection Title:Policy Research working paper ; no. WPS 4632
Country:World; Date Stored:2008/05/27
Document Date:2008/05/01Document Type:Policy Research Working Paper
SubTopics:Econometrics; Access to Finance; Economic Theory & Research; Currencies and Exchange Rates; Statistical & Mathematical SciencesLanguage:English
Region:The World RegionReport Number:WPS4632
Volume No:1 of 1  

Summary: The validity of instrumental variables (IV) regression models depends crucially on fundamentally untestable exclusion restrictions. Typically exclusion restrictions are assumed to hold exactly in the relevant population, yet in many empirical applications there are reasonable prior grounds to doubt their literal truth. In this paper I show how to incorporate prior uncertainty about the validity of the exclusion restriction into linear IV models, and explore the consequences for inference. In particular 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. The author illustrates these points using several prominent recent empirical papers that use linear IV models.

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