Generalized Moments Estimation For Spatial Panel Data
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Generalized Moments Estimation for Spatial Panel Data
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.
Estimation of Spatial Panels
Estimation of Spatial Panels provides some recent developments on the specification and estimation of spatial panel models.