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Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming

Author

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  • Viliam Druska

    (Charles Univeristy)

  • William C. Horrace

    (Syracuse University)

Abstract

We consider estimation of a panel data model where disturbances are spatially correlated in the cross-sectional dimension, based on geographic or economic proximity. When the time dimension of the data is large, spatial correlation parameters may be consistently estimated. When the time dimension is small (the usual panel data case), we develop an estimator that extends the cross-sectional model of Kelejian and Prucha. This approach is applied in a stochastic frontier framework to a panel of Indonesian rice farms where spatial correlations represent productivity shock spillovers, based on geographic proximity and weather. These spillovers affect farm-level efficiency estimation and ranking.

Suggested Citation

  • Viliam Druska & William C. Horrace, 2002. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," Econometrics 0206004, University Library of Munich, Germany, revised 11 May 2003.
    • Handle: RePEc:wpa:wuwpem:0206004
    Note: Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP; pages: 37; figures: included. Spatial GMM for panel data applied to a stochastic frontier model
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    autocorrelation; Moran I; productivity; stochastic frontier; spatial dependence;
    All these keywords.

    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

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