Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming
AbstractWe 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.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0206004.
Length: 37 pages
Date of creation: 19 Jun 2002
Date of revision: 11 May 2003
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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autocorrelation; Moran I; productivity; stochastic frontier; spatial dependence;
Other versions of this item:
- Viliam Druska & William C. Horrace, 2004. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 185-198.
- 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; Longitudinal Data; Spatial Time Series
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