Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming
提出一种空间面板数据模型的估计方法，适用于时间维度小的情况，并应用于印度尼西亚水稻农场数据，发现基于地理和天气的空间相关性影响效率估计与排名。
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. Copyright 2004, Oxford University Press.