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Instrumental variables regressions with honestly uncertain exclusion restrictions

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  • Kraay, Aart

Abstract

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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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 4632.

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Date of creation: 01 May 2008
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Handle: RePEc:wbk:wbrwps:4632

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Keywords: Economic Theory&Research; Statistical&Mathematical Sciences; Currencies and Exchange Rates; Econometrics; Access to Finance;

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  1. Small, Dylan S., 2007. "Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1049-1058, September.
  2. Raghuram G. Rajan & Luigi Zingales, . "Financial Dependence and Growth," CRSP working papers 344, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  3. Daron Acemoglu & Simon Johnson & James A. Robinson, 2000. "The Colonial Origins of Comparative Development: An Empirical Investigation," NBER Working Papers 7771, National Bureau of Economic Research, Inc.
  4. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(04), pages 483-509, August.
  5. Rodríguez, Francisco & Rodrik, Dani, 1999. "Trade Policy and Economic Growth: A Sceptic's Guide to the Cross-National Evidence," CEPR Discussion Papers 2143, C.E.P.R. Discussion Papers.
  6. Hoogerheide, L.F. & Kleibergen, F.R. & van Dijk, H.K., 2006. "Natural conjugate priors for the instrumental variables regression model applied to the Angrist-Krueger data," Econometric Institute Research Papers EI 2006-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  7. Edward L. Glaeser & Rafael La Porta & Florencio Lopez-de-Silane & Andrei Shleifer, 2004. "Do Institutions Cause Growth?," NBER Working Papers 10568, National Bureau of Economic Research, Inc.
  8. Michael P. Murray, 2006. "Avoiding Invalid Instruments and Coping with Weak Instruments," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 111-132, Fall.
  9. Mehmet Caner & Dan Berkowitz & Ying Fang, 2006. "Are Nearly Exogenous Instruments Reliable?," Working Papers 207, University of Pittsburgh, Department of Economics, revised Jan 2006.
  10. David H. Romer & Jeffrey A. Frankel, 1999. "Does Trade Cause Growth?," American Economic Review, American Economic Association, vol. 89(3), pages 379-399, June.
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Cited by:
  1. Fedderke, Johannes & Klitgaard, Robert, 2013. "How Much Do Rights Matter?," World Development, Elsevier, vol. 51(C), pages 187-206.
  2. Berkowitz, Daniel & Hoekstra, Mark, 2011. "Does high school quality matter? Evidence from admissions data," Economics of Education Review, Elsevier, vol. 30(2), pages 280-288, April.
  3. Michal Kolesár & Raj Chetty & John N. Friedman & Edward L. Glaeser & Guido W. Imbens, 2011. "Identification and Inference with Many Invalid Instruments," NBER Working Papers 17519, National Bureau of Economic Research, Inc.

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