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Optimal Inference for Instrumental Variables Regression with non-Gaussian Errors

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Author Info
Mathias D. Cattaneo
Richard K. Crump
Michael Jansson () (School of Economics and Management, University of Aarhus, Denmark and CREATES)

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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.

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Publisher Info
Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2007-11.

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Length: 43
Date of creation: 25 Jun 2007
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Handle: RePEc:aah:create:2007-11

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Related research
Keywords: Instrumental variables regression; weak instruments; adaptive estimation;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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