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A consistent nonparametric estimation of spatial autocovariances

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  • Théophile Azomahou

    (BETA, Université Louis Pasteur, Strasbourg 1)

  • Dong Li

    (Department of Economics, Kansas State University)

Abstract

We examine some aspects of estimating sample autocovariances for spatial processes. Especially, we note that for such processes, it is not possible to approximate the expectation by the sample mean, like in the case of time series data. Then, we propose a consistent nonparametric estimation of sample autocovariances for an irregularly scattered spatial process, derived from a transformation of the initial process. We also suggest an L_2-consistent weighting matrix. Monte Carlo simulations are used to evaluate the performance of the proposed estimators in finite samples.

Suggested Citation

  • Théophile Azomahou & Dong Li, 2005. "A consistent nonparametric estimation of spatial autocovariances," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-05c10005
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. Chen, Xiaoheng & Conley, Timothy G., 2001. "A new semiparametric spatial model for panel time series," Journal of Econometrics, Elsevier, vol. 105(1), pages 59-83, November.
    4. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    5. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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