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Optimal inference for instrumental variables regression with non-Gaussian errors

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Author Info

  • Cattaneo, Matias D.
  • Crump, Richard K.
  • Jansson, Michael

Abstract

This paper is concerned with inference on the coefficient on the endogenous regressor in a linear instrumental variables model with a single endogenous regressor, nonrandom exogenous regressors and instruments, and i.i.d. errors whose distribution is unknown. It is shown that under mild smoothness conditions on the error distribution it is possible to develop tests which are “nearly” efficient in the sense of Andrews et al. (2006) when identification is weak and consistent and asymptotically optimal when identification is strong. In addition, an estimator is presented which can be used in the usual way to construct valid (indeed, optimal) confidence intervals when identification is strong. The estimator is of the two stage least squares variety and is asymptotically efficient under strong identification whether or not the errors are normal.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 167 (2012)
Issue (Month): 1 ()
Pages: 1-15

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Handle: RePEc:eee:econom:v:167:y:2012:i:1:p:1-15

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Web page: http://www.elsevier.com/locate/jeconom

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  1. Nelson, C. & Startz, R., 1988. "The Distribution Of The Instrumental Variables Estimator And Its T-Ratio When The Instrument Is A Poor One," Working Papers 88-07, University of Washington, Department of Economics.
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Cited by:
  1. David Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
  2. Patrik Guggenberger, . "Applications of Subsampling, Hybrid, and Size-Correction Methods (joint with D.W.K. Andrews), 2005, this version May 2007," UCLA Economics Online Papers 414, UCLA Department of Economics.
  3. Andrews, Donald W.K. & Guggenberger, Patrik, 2010. "Applications of subsampling, hybrid, and size-correction methods," Journal of Econometrics, Elsevier, vol. 158(2), pages 285-305, October.

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