Generalized Moments Estimation for Panel Data

Viliam Druska and William Horrace

No 291, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper considers estimation of a panel data model with disturbances that are autocorrelated across cross-sectional units. It is assumed that the disturbances are spatially correlated, based on some geographic or economic proximity measure. If the time dimension of the data is large, feasible and efficient estimation proceeds by using the time dimension to estimate spatial dependence parameters. For the case where the time dimension is small (the usual panel data case), we develop a generalized moments estimation approach that is a straight-forward generalization of a cross-sectional model due to Kelejian and Prucha. We apply this approach in a stochastic frontier framework to a panel of Indonesian rice farms where spatial correlations are based on geographic proximity, altitude and weather. The correlations represent productivity shock spillovers across the rice farms in different villages on the island of Java. Test statistics indicate that productivity shock spillovers may exist in this (and perhaps other) data sets, and that these spillovers have effects on technical efficiency estimation and ranking.

JEL-codes: C21 C23 (search for similar items in EconPapers)
Date: 2003-03
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: TWP
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Citations: View citations in EconPapers (4)

Published as Druska, Villiam and William C. Horrace. "Generalized Moments Estimation For Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, 2003, v86(1,Feb), 185-198.

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