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Optimal Inference for Instrumental Variables Regression with non-Gaussian Errors

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  • Mathias D. Cattaneo
  • Richard K. Crump
  • Michael Jansson

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

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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Bibliographic 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
Date of revision:
Handle: RePEc:aah:create:2007-11

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Web page: http://www.econ.au.dk/afn/

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

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References

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  1. Steigerwald, Douglas G., 1992. "On the finite sample behavior of adaptive estimators," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 371-400.
  2. Donald W. K. Andrews & Marcelo J. Moreira & James H. Stock, 2006. "Optimal Two-Sided Invariant Similar Tests for Instrumental Variables Regression," Econometrica, Econometric Society, vol. 74(3), pages 715-752, 05.
  3. Charles R. Nelson & Richard Startz, 1988. "The Distribution of the Instrumental Variables Estimator and Its t-RatioWhen the Instrument is a Poor One," NBER Technical Working Papers 0069, National Bureau of Economic Research, Inc.
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  6. Donald W.K. Andrews & Vadim Marmer, 2005. "Exactly Distribution-free Inference in Instrumental Variables Regression with Possibly Weak Instruments," Cowles Foundation Discussion Papers 1501, Cowles Foundation for Research in Economics, Yale University.
  7. Chao, John Chao & Norman R. Swanson, 2003. "Consistent Estimation with a Large Number of Weak Instruments," Cowles Foundation Discussion Papers 1417, Cowles Foundation for Research in Economics, Yale University.
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  28. repec:cup:cbooks:9780521496032 is not listed on IDEAS
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Citations

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Cited by:
  1. Donald W.K. Andrews & Patrik Guggenberger, 2007. "Applications of Subsampling, Hybrid, and Size-Correction Methods," Cowles Foundation Discussion Papers 1608, Cowles Foundation for Research in Economics, Yale University.
  2. David Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
  3. 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.

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