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

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  • Kelejian, Harry H
  • Prucha, Ingmar R
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 this 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. Copyright 1999 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Suggested Citation

  • Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    • Handle: RePEc:ier:iecrev:v:40:y:1999:i:2:p:509-33
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    References listed on IDEAS

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    1. 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 466-474, August.
    2. 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.
    3. Harry H. Kelejian & Dennis P. Robinson, 1993. "A Suggested Method Of Estimation For Spatial Interdependent Models With Autocorrelated Errors, And An Application To A County Expenditure Model," Papers in Regional Science, Wiley Blackwell, vol. 72(3), pages 297-312, July.
    4. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    5. 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.
    6. 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 675-683, May.
    7. 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 334-338, May.
    8. Luc Anselin & Raymond J. G. M. Florax, 1995. "Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax (ed.), New Directions in Spatial Econometrics, chapter 2, pages 21-74, Springer.
    9. Benedikt M. Pötscher & Ingmar R. Prucha, 1999. "Basic Elements of Asymptotic Theory," Electronic Working Papers 99-001, University of Maryland, Department of Economics.
    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.
    11. J. Bradford De Long & Lawrence H. Summers, 1991. "Equipment Investment and Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(2), pages 445-502.
    12. 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 957-979, November.
    13. Harry H. Kelejian & Dennis P. Robinson, 1995. "Spatial Correlation: A Suggested Alternative to the Autoregressive Model," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax (ed.), New Directions in Spatial Econometrics, chapter 3, pages 75-95, Springer.
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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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