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Empirical evidence concerning the finite sample performance of El-type structural equation estimation and inference methods

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Author Info

  • Mittelhammer, Ronald C.
  • Judge, George G.

    ()
    (University of California, Berkeley. Dept of agricultural and resource economics and policy)

  • Schoenberg, Ron

Abstract

This paper presents empirical evidence concerning the finite sample performance of conventional and generalized empirical likelihood-type estimators that utilize instruments in the context of linear structural models characterized by endogenous explanatory variables. There are suggestions in the literature that traditional and non-traditional asymptotically efficient estimators based on moment equations may, for the relatively small sample sizes usually encountered in econometric practice, have relatively large biases and/or variances and provide an inadequate basis for estimation and inference. Given this uncertainty we use a range of data sampling processes and Monte Carlo sampling procedures to accumulate finite sample empirical evidence concerning these questions for a family of generalized empirical likelihood-type estimators in comparison to conventional 2SLS and GMM estimators. Solutions to EL-type empirical momentconstrained optimization problems present formidable numerical challenges. We identify effective optimization algorithms for meeting these challenges.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 945.

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Length: 36 pages
Date of creation: 2003
Date of revision:
Handle: RePEc:are:cudare:945

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Related research

Keywords: econometric models estimation theory moments method monte carlo analysis;

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References

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  1. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  2. Guido W. Imbens & Richard H. Spady & Phillip Johnson, 1998. "Information Theoretic Approaches to Inference in Moment Condition Models," Econometrica, Econometric Society, vol. 66(2), pages 333-358, March.
  3. Nelson, C. & Startz, R., 1988. "Some Furthere Results On The Exact Small Sample Properties Of The Instrumental Variable Estimator," Working Papers 88-06, University of Washington, Department of Economics.
  4. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  5. Mittelhammer,Ron C. & Judge,George G. & Miller,Douglas J., 2000. "Econometric Foundations Pack with CD-ROM," Cambridge Books, Cambridge University Press, number 9780521623940, October.
  6. Judge, G.G. & Bock, M.E., 1983. "Biased estimation," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 10, pages 599-649 Elsevier.
  7. Maddala, G S & Jeong, Jinook, 1992. "On the Exact Small Sample Distribution of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 60(1), pages 181-83, January.
  8. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
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Citations

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Cited by:
  1. Umut Oguzoglu & Thanasis Stengos, 2005. "Can Dynamic Panel Data Explain the Finance-Growth Link? An Empirical Likelihood Approach," Working Papers 0502, University of Guelph, Department of Economics and Finance.
  2. Giuseppe Ragusa, 2011. "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 406-456.
  3. Grendar, Marian & Judge, George G., 2006. "Large Deviations Theory and Empirical Estimator Choice," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt20n3j23r, Department of Agricultural & Resource Economics, UC Berkeley.
  4. Lauren Bin Dong & David E. A. Giles, 2004. "An Empirical Likelihood Ratio Test for Normality," Econometrics Working Papers 0401, Department of Economics, University of Victoria.
  5. Lauren Bin Dong, 2004. "The Behrens-Fisher Problem: An Empirical Likelihood Ratio Approach," Econometrics Working Papers 0404, Department of Economics, University of Victoria.
  6. repec:ebl:ecbull:v:3:y:2005:i:13:p:1-6 is not listed on IDEAS
  7. Patrik Guggenberger, 2006. "Finite-Sample Evidence Suggesting a Heavy Tail Problem of the Generalized Empirical Likelihood Estimator, accepted for publication, Econometric Reviews," UCLA Economics Online Papers 371, UCLA Department of Economics.
  8. Judge, George G. & Mittelhammer, Ronald C, 2004. "Estimating the link function in multinomial response models under endogeneity and quadratic loss," CUDARE Working Paper Series 0970, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  9. Miller, Douglas J. & Mittelhammer, Ronald C. & Judge, George G., 2004. "Entropy-Based Estimation And Inference In Binary Response Models Under Endogeneity," 2004 Annual meeting, August 1-4, Denver, CO 20319, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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