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Conditional Moment Models under Semi-Strong Identification

We consider models defined by conditional moment restrictions under semi-strong identification. Identification strength is directly defined through the conditional mo- ments that flatten as the sample size increases. The framework allows for different iden- tification strengths across parameter’s components. We propose a minimum distance estimator that is robust to semi-strong identification and does not rely on the choice of a user-chosen parameter, such as the number of instruments or any other smoothing parameter. Our method yields consistent and asymptotically normal estimators of each parameter’s components. Heteroskedasticity-robust inference is possible through Wald testing without prior knowledge of the identification pattern. In simulations, we find that our estimator is competitive with alternative estimators based on many instruments. In particular, it is well-centered with better coverage rates for confidence intervals.

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File URL: http://www.sfu.ca/econ-research/RePEc/sfu/sfudps/dp11-04.pdf
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Paper provided by Department of Economics, Simon Fraser University in its series Discussion Papers with number dp11-04.

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Length: 46
Date of creation: Sep 2011
Date of revision: Dec 2012
Handle: RePEc:sfu:sfudps:dp11-04
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Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

Phone: (778)782-3508
Fax: (778)782-5944
Web page: http://www.sfu.ca/economics.html

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  13. Jun, Sung Jae & Pinkse, Joris, 2009. "Semiparametric tests of conditional moment restrictions under weak or partial identification," Journal of Econometrics, Elsevier, vol. 152(1), pages 3-18, September.
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  15. Bertille Antoine & Eric Renault, 2009. "Efficient GMM with nearly-weak instruments," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S135-S171, 01.
  16. Christian Hansen & Jerry Hausman & Whitney Newey, 2006. "Estimation with many instrumental variables," CeMMAP working papers CWP19/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  17. Lavergne, Pascal & Patilea, Valentin, 2013. "Smooth minimum distance estimation and testing with conditional estimating equations: Uniform in bandwidth theory," Journal of Econometrics, Elsevier, vol. 177(1), pages 47-59.
  18. Joshua D. Angrist & Guido W. Imbens & Alan Krueger, 1995. "Jackknife Instrumental Variables Estimation," NBER Technical Working Papers 0172, National Bureau of Economic Research, Inc.
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