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Random Effects Estimators with many Instrumental Variables

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  • Gary Chamberlain
  • Guido Imbens

Abstract

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random coefficients is modelled as an unknown parameter. In addition to proposing a new estimator, our analysis yields new insights into the properties of the standard two-stage least squares (TSLS) and limited-information maximum likelihood (LIML) estimators in the case with many weak instruments. We show that in some interesting cases, TSLS and LIML can be approximated by maximizing the random effects likelihood subject to particular constraints. We show that statistics based on comparisons of the unconstrained estimates of these parameters to the implicit TSLS and LIML restrictions can be used to identify settings when standard large sample approximations to the distributions of TSLS and LIML are likely to perform poorly. We also show that with many weak instruments, LIML confidence intervals are likely to have under-coverage, even though its finite sample distribution is approximately centered at the true value of the parameter. In an application with real data and simulations around this data set, the proposed estimator performs markedly better than TSLS and LIML, both in terms of coverage rate and in terms of risk. Copyright Econometric Society 2004.

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 72 (2004)
Issue (Month): 1 (01)
Pages: 295-306

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Handle: RePEc:ecm:emetrp:v:72:y:2004:i:1:p:295-306

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Cited by:
  1. Strittmatter, Anthony & Sunde, Uwe, 2011. "Health and Economic Development - Evidence from the Introduction of Public Health Care," Economics Working Paper Series 1132, University of St. Gallen, School of Economics and Political Science.
  2. Manuel Arellano & Stéphane Bonhomme, 2009. "Robust Priors in Nonlinear Panel Data Models," Econometrica, Econometric Society, vol. 77(2), pages 489-536, 03.
  3. Grant Hillier & Giovanni Forchini, 2004. "Ill-posed Problems and Instruments' Weakness," Econometric Society 2004 Australasian Meetings 357, Econometric Society.
  4. Canay, Ivan A., 2010. "Simultaneous selection and weighting of moments in GMM using a trapezoidal kernel," Journal of Econometrics, Elsevier, vol. 156(2), pages 284-303, June.
  5. Richard Startz & Charles Nelson & Eric Zivot, 1999. "Improved Inference for the Instrumental Variable Estimator," Econometrics 9905001, EconWPA.
  6. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York.
  7. Kasey Buckles & Daniel M. Hungerman, 2008. "Season of Birth and Later Outcomes: Old Questions, New Answers," NBER Working Papers 14573, National Bureau of Economic Research, Inc.
  8. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.

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