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Minimum distance estimation of the spatial panel autoregressive model

Author

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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.)

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.

Suggested Citation

  • Théophile Azomahou, 2008. "Minimum distance estimation of the spatial panel autoregressive model," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 2(1), pages 49-83, April.
  • Handle: RePEc:afc:cliome:v:2:y:2008:i:1:p:49-83
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    References listed on IDEAS

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    1. 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.
    2. Deaton, Angus, 1990. "Price elasticities from survey data : Extensions and Indonesian results," Journal of Econometrics, Elsevier, vol. 44(3), pages 281-309, June.
    3. Julie A. Hewitt & W. Michael Hanemann, 1995. "A Discrete/Continuous Choice Approach to Residential Water Demand under Block Rate Pricing," Land Economics, University of Wisconsin Press, vol. 71(2), pages 173-192.
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    5. 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.
    6. 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 229-243, July-Sept.
    7. 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.
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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).

    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

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