Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming

Viliam Druska and William C. Horrace ()
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Viliam Druska: Charles Univeristy

Econometrics from EconWPA

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.

Keywords: autocorrelation; Moran I; productivity; stochastic frontier; spatial dependence (search for similar items in EconPapers)
JEL-codes: C13 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2002-06-19, Revised 2003-05-11
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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http://129.3.20.41/eps/em/papers/0206/0206004.pdf (application/pdf)

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Journal Article: Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming (2004)
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0206004

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