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A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

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Author Info

  • Harry H. Kelejian

    ()
    (Department of Economics, University of Maryland)

  • Ingmar R. Prucha

    ()
    (Department of Economics, University of Maryland)

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    Abstract

    This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in the paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate or large sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. We provide results concerning the large and small sample properties of this estimator.

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    File URL: http://www.econweb.umd.edu/papers/pk1.pdf
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    Bibliographic Info

    Paper provided by University of Maryland, Department of Economics in its series Electronic Working Papers with number 95-001.

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    Date of creation: Feb 1995
    Date of revision: Mar 1997
    Handle: RePEc:umd:umdeco:95-001

    Contact details of provider:
    Postal: Department of Economics, University of Maryland, Tydings Hall, College Park, MD 20742
    Web page: http://www.econ.umd.edu/

    Order Information:
    Postal: Ms. Elizabeth Martinez, Department of Economics, University of Maryland, Tydings Hall, College Park, MD 20742
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    Related research

    Keywords: Spatial Models; Autocorrelation; Generalized Moments Estimator; Feasible GLS;

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    References

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    1. J. Bradford De Long & Lawrence H. Summers, 1990. "Equipment Investment and Economic Growth," NBER Working Papers 3515, National Bureau of Economic Research, Inc.
    2. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-38, May.
    3. Mandy, David M & Martins-Filho, Carlos, 1994. "A Unified Approach to Asymptotic Equivalence of Aitken and Feasible Aitken Instrumental Variables Estimators," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(4), pages 957-79, November.
    4. Heijmans, Risto D. H. & Magnus, Jan R., 1986. "Consistent maximum-likelihood estimation with dependent observations : The general (non-normal) case and the normal case," Journal of Econometrics, Elsevier, vol. 32(2), pages 253-285, July.
    5. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
    6. Potscher, Benedikt M. & Prucha, Ingmar R., 1986. "A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations," Journal of Econometrics, Elsevier, vol. 32(2), pages 219-251, July.
    7. Benedikt M. Pötscher & Ingmar R. Prucha, 1999. "Basic Elements of Asymptotic Theory," Electronic Working Papers 99-001, University of Maryland, Department of Economics.
    8. Dubin, Robin A, 1988. "Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms," The Review of Economics and Statistics, MIT Press, vol. 70(3), pages 466-74, August.
    9. Potscher, Benedikt M & Prucha, Ingmar R, 1989. "A Uniform Law of Large Numbers for Dependent and Heterogeneous Data Processes," Econometrica, Econometric Society, vol. 57(3), pages 675-83, May.
    10. Anselin, Luc, 1990. "Some robust approaches to testing and estimation in spatial econometrics," Regional Science and Urban Economics, Elsevier, vol. 20(2), pages 141-163, September.
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