Optimal Inference for Instrumental Variables Regression with non-Gaussian Errors

Matias Cattaneo, Richard Crump and Michael Jansson

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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 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.

Keywords: Instrumental variables regression; weak instruments; adaptive estimation (search for similar items in EconPapers)
JEL-codes: C14 C31 (search for similar items in EconPapers)
Pages: 43
Date: 2007-06-25
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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Journal Article: Optimal inference for instrumental variables regression with non-Gaussian errors (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2007-11

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