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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
Ron Mittelhammer (Washington State University)
George Judge (University of California, Berkeley and Giannini Foundation)
Ron Schoenberg (Aptech Systems, Inc.)

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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.

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Paper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number 945.

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Date of creation: 01 Jan 2003
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Handle: RePEc:cdl:agrebk:945

Note: oai:cdlib1:are_ucb-1036
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Related research
Keywords: Unbiased moment based estimation and inference; empirical likelihood; empirical exponential likelihood; semiparametric models; conditional estimating equations; finite sample bias and precision; squared error loss; instrumental conditioning variables;

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References listed on IDEAS
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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.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Lauren Bin Dong, 2004. "The Behrens-Fisher Problem: An Empirical Likelihood Ratio Approach," Econometrics Working Papers 0404, Department of Economics, University of Victoria. [Downloadable!]
  2. Patrik Guggenberger, 2005. "Monte-carlo evidence suggesting a no moment problem of the continuous updating estimator," Economics Bulletin, Economics Bulletin, vol. 3(13), pages 1-6. [Downloadable!]
  3. 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. [Downloadable!]
  4. George Judge & Ron Mittelhammer, 2004. "Estimating the Link Function in Multinomial Response Models under Endogeneity and Quadratic Loss," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series 970, Department of Agricultural & Resource Economics, UC Berkeley. [Downloadable!]
  5. 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. [Downloadable!]
  6. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2006. "A New Light from Old Wisdoms : Alternative Estimation Methods of Simultaneous Equations with Possibly Many Instruments," CIRJE F-Series CIRJE-F-399, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
  7. Miller, Douglas & Mittelhammer, Ron & Judge, George, 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). [Downloadable!]
  8. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On Finite Sample Properties of Alternative Estimators of Coefficients in a Structural Equation with Many Instruments," CIRJE F-Series CIRJE-F-577, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
  9. Richard Smith, 2005. "Local GEL methods for conditional moment restrictions," CeMMAP working papers CWP15/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  10. Giuseppe Ragusa, 2008. "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions," Working Papers 080906, University of California-Irvine, Department of Economics. [Downloadable!]
  11. 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. [Downloadable!]
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