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Minimum distance estimation of the spatial panel autoregressive model

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  • Théophile Azomahou

    ()
    (Bureau d’Économie Théorique et Appliquée (BETA-Theme), Université Louis Pasteur, 61, avenue de la Forêt Noire, 67085 Strasbourg Cedex, France.)

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    Abstract

    This paper contributes to the interface literature of new methodological foundation of analyzing historical data with space and spatio-temporal phenomena. In particular, I consider estimating the spatial panel autoregressive model using the minimum distance estimator. Spatial autoregression has important implications for economic system that typifies correlatedness across many spatial locations and which could evolve over long span of time. To overcome computational difficulties, I suggest a two-stage estimation procedure based on minimum distance estimators. A striking feature of the proposed model is that minimum distance estimates are derived under common slopes and complete equality of parameters across spatial units. Assumption of common slopes across spatial units is an empirical and theoretical plausibility as many spatial units are observed to share common trend and typology of changes occurring to the individual system under which equality of parameters are possibilities. The estimation strategy allows various restrictions on time-varying vector parameters. Moreover, those restrictions can easily be tested. I apply this procedure to the residential demand for water of 115 French municipalities over the biannual period 1988–1993. The primary contribution of the paper is to the methodological side of cliometrics while the empirical application (with shorter time period) has been presented for illustrative purpose although, it can nonetheless be readily applied to historical data with long-time horizon allowing for restrictions such as spatio-temporal common vector and structural break in parameter estimates.

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    Bibliographic Info

    Article provided by Association Française de Cliométrie (AFC) in its journal Cliometrica, Journal of Historical Economics and Econometric History.

    Volume (Year): 2 (2008)
    Issue (Month): 1 (April)
    Pages: 49-83

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    Handle: RePEc:afc:cliome:v:2:y:2008:i:1:p:49-83

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    Web page: http://www.cliometrie.org
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    Related research

    Keywords: Spatial dependence; Panel data; Minimum distance estimator; Residential demand for water;

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    References

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    1. Deaton, A., 1988. "Price Elasticities From Survey Data: Extensions And Indonesian Results," Papers 138, Princeton, Woodrow Wilson School - Development Studies.
    2. 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.
    3. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
    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.
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    7. 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.
    8. Lars Gårn Hansen, 1996. "Water and Energy Price Impacts on Residential Water Demand in Copenhagen," Land Economics, University of Wisconsin Press, vol. 72(1), pages 66-79.
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    10. Joris Pinkse & Margaret E. Slade & Craig Brett, 2002. "Spatial Price Competition: A Semiparametric Approach," Econometrica, Econometric Society, vol. 70(3), pages 1111-1153, May.
    11. Dowd, Michael R. & LeSage, James P., 1997. "Analysis of spatial contiguity influences on state price level formation," International Journal of Forecasting, Elsevier, vol. 13(2), pages 245-253, June.
    12. Kodde, D A & Palm, Franz C & Pfann, G A, 1990. "Asymptotic Least-Squares Estimation Efficiency Considerations and Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(3), pages 229-43, July-Sept.
    13. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    14. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
    15. Yoo, Seung-Hoon & Yang, Chang-Young, 2000. "Dealing with bottled water expenditures data with zero observations: a semiparametric specification," Economics Letters, Elsevier, vol. 66(2), pages 151-157, February.
    16. James P. Ziliak & Beth A. Wilson & Joe A. Stone, 1999. "Spatial Dynamics And Heterogeneity In The Cyclicality Of Real Wages," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 227-236, May.
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    Cited by:
    1. Henrique Monteiro, 2010. "Residential Water Demand in Portugal: checking for efficiency-based justifications for increasing block tariffs," Working Papers Series 1 ercwp0110, ISCTE-IUL, Business Research Unit (BRU-IUL).

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