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GMM Estimation of Lattice Models Using Panel Data: Application

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  • Théophile Azomahou

Abstract

We propose an empirical application of lattice models to actual household-level data based on the generalized method of moments. We take advantage of the two dimensional structure of panel data to construct a lattice specification. Then, a class of nonparametric, positive semidefinite covariance matrix estimators that allow for a general form of spatial dependence characterized by a metric of economic distance is introduced. This framework is applied to estimating spatial patterns in the residential demand for drinking water. Estimation results indicate that accounting for spatial dependence yields efficient estimate of the asymptotic variance matrix. Compared to non-spatial strategies, spatial dependence implies higher standard errors for all parameter estimates so as to strongly modify patterns of significance.

Suggested Citation

  • Théophile Azomahou, 2001. "GMM Estimation of Lattice Models Using Panel Data: Application," Working Papers of BETA 2001-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  • Handle: RePEc:ulp:sbbeta:2001-09
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    References listed on IDEAS

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    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. DeSarbo, Wayne S. & Kim, Youngchan & Fong, Duncan, 1998. "A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 79-108, November.
    3. Conrad, Klaus & Seitz, Helmut, 1997. "Infrastructure provision and international market share rivalry," Regional Science and Urban Economics, Elsevier, vol. 27(6), pages 715-734, November.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, pages 817-858.
    5. 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.
    6. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
    7. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
    8. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    9. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    10. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    11. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, pages 285-307.
    12. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    13. Maital, Shlomo, 1978. "Multidimensional scaling : Some econometric applications," Journal of Econometrics, Elsevier, vol. 8(1), pages 33-46, August.
    14. 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.
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    More about this item

    Keywords

    Lattice models; Spatial dependence; Nonparametric covariance matrix estimation; Residential demand for water;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D10 - Microeconomics - - Household Behavior - - - General

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