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A new method of projection-based inference in GMM with weakly identified nuisance parameters

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  • Chaudhuri, Saraswata
  • Zivot, Eric

Abstract

Projection-based tests for subsets of parameters are useful because they do not over-reject the true parameter values when either it is difficult to estimate the nuisance parameters or their identification status is questionable. However, they are also often criticized for being overly conservative. We overcome this conservativeness by introducing a new projection-based test that is more powerful than the traditional projection-based tests. The new test is even asymptotically equivalent to the related plug-in-based tests when all the parameters are identified. Extension to models with weakly identified parameters shows that the new test is not dominated by the related plug-in-based tests.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 164 (2011)
Issue (Month): 2 (October)
Pages: 239-251

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Handle: RePEc:eee:econom:v:164:y:2011:i:2:p:239-251

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Projection test Nuisance parameters C([alpha]) statistic GMM Weak identification;

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References

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  1. Frank Kleibergen, 2001. "Testing Parameters in GMM without Assuming that they are identified," Tinbergen Institute Discussion Papers 01-067/4, Tinbergen Institute.
  2. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  3. Chaudhuri, Saraswata & Richardson, Thomas & Robins, James & Zivot, Eric, 2010. "A New Projection-Type Split-Sample Score Test In Linear Instrumental Variables Regression," Econometric Theory, Cambridge University Press, vol. 26(06), pages 1820-1837, December.
  4. DUFOUR, Jean-Marie & TAAMOUTI, Mohamed, 2003. "Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments," Cahiers de recherche 08-2003, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  5. Mikusheva, Anna, 2010. "Robust confidence sets in the presence of weak instruments," Journal of Econometrics, Elsevier, vol. 157(2), pages 236-247, August.
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  10. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
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  12. Dufour, Jean-Marie & Taamouti, Mohamed, 2007. "Further results on projection-based inference in IV regressions with weak, collinear or missing instruments," Journal of Econometrics, Elsevier, vol. 139(1), pages 133-153, July.
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Citations

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Cited by:
  1. Adam McCloskey, 2012. "Bonferroni-Based Size-Correction for Nonstandard Testing Problems," Working Papers 2012-16, Brown University, Department of Economics.
  2. Mikusheva, Anna, 2013. "Survey on statistical inferences in weakly-identified instrumental variable models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 29(1), pages 117-131.
  3. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2010. "On the precision of Calvo parameter estimates in structural NKPC models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1582-1595, September.
  4. Bertille Antoine & Eric Renault, 2012. "Testing Identification Strength," Discussion Papers dp12-17, Department of Economics, Simon Fraser University, revised Feb 2013.
  5. Firmin Doko Tchatoka, 2011. "Subset hypotheses testing and instrument exclusion in the linear IV regression," Working Papers 10668, University of Tasmania, School of Economics and Finance.
  6. Guggenberger, Patrik & Ramalho, Joaquim J.S. & Smith, Richard J., 2012. "GEL statistics under weak identification," Journal of Econometrics, Elsevier, vol. 170(2), pages 331-349.
  7. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2009. "Frequentist inference in weakly identified DSGE models," Working Papers 09-13, Federal Reserve Bank of Philadelphia.
  8. Xu Cheng, 2014. "Uniform Inference in Nonlinear Models with Mixed Identification Strength," PIER Working Paper Archive 14-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  9. Noud P.A. van Giersbergen, 2011. "Bootstrapping Subset Test Statistics in IV Regression," UvA-Econometrics Working Papers 11-08, Universiteit van Amsterdam, Dept. of Econometrics.

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