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Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming

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  • Viliam Druska
  • William C. Horrace

Abstract

We consider estimation of a panel data model where disturbances are spatially correlated in the crosssectional 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.

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  • Viliam Druska & William C. Horrace, 2004. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, John Wiley & Sons, vol. 86(1), pages 185-198, February.
    • Handle: RePEc:wly:ajagec:v:86:y:2004:i:1:p:185-198
    DOI: 10.1111/j.1467-8276.2004.tb18465.x
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    1. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    2. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    3. 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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