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Empirical Likelihood Estimators Of The Linear Simultaneous Equations Model

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  • Marsh, Thomas L.
  • Mittelhammer, Ronald C.
  • Judge, George G.

Abstract

Information theoretic estimators are specified for a system of linear simultaneous equations, including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood. Monte Carlo experiments are used to compare finite sample performance of these estimators to traditional generalized method of moments.

Suggested Citation

  • Marsh, Thomas L. & Mittelhammer, Ronald C. & Judge, George G., 2001. "Empirical Likelihood Estimators Of The Linear Simultaneous Equations Model," 2001 Annual meeting, August 5-8, Chicago, IL 20752, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea01:20752
    DOI: 10.22004/ag.econ.20752
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    References listed on IDEAS

    as
    1. 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.
    2. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
    3. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    4. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
    5. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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