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Generalized Moment Based Estimation and Inference

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  • George Judge

    (University of California)

  • Marco Van_Akkeren

    (University of California)

Abstract

We extend the empirical likelihood method of estimation and inference proposed by Owen and others and demonstrate how it may be used in a general linear model context and to mitigate the impact of an ill-conditioned design matrix. A dual loss information theoretic estimating function is used along with extended moment conditions to yield a data based estimator that has the usual consistency and asymptotic normality results. Limiting chi-square distributions may be used to obtain hypothesis test or confidence intervals. The estimator appears to have excellent finite sample properties under a squared error loss measure.

Suggested Citation

  • George Judge & Marco Van_Akkeren, 2000. "Generalized Moment Based Estimation and Inference," Econometric Society World Congress 2000 Contributed Papers 0073, Econometric Society.
  • Handle: RePEc:ecm:wc2000:0073
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    References listed on IDEAS

    as
    1. Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
    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. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics,in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
    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. Dey, Dipak K. & Ghosh, Malay & Strawderman, William E., 1999. "On estimation with balanced loss functions," Statistics & Probability Letters, Elsevier, vol. 45(2), pages 97-101, November.
    6. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    7. Zellner, A., 1992. "Bayesian and Non-Bayesian Estimation using Balanced Loss Functions," Papers 92-20, California Irvine - School of Social Sciences.
    8. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

    1. Femenia, Fabienne & Gohin, Alexandre, 2007. "Estimating price elasticities of food trade functions: How relevant is the gravity approach?," Working Papers 7211, TRADEAG - Agricultural Trade Agreements.
    2. Alexandre Gohin & Fabienne Féménia, 2009. "Estimating Price Elasticities of Food Trade Functions: How Relevant is the CES-based Gravity Approach?," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(2), pages 253-272.
    3. Fabienne Féménia & Alexandre Gohin, 2009. "Estimating censored and non homothetic demand systems: the generalized maximum entropy approach," Working Papers SMART - LERECO 09-12, INRA UMR SMART-LERECO.

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