Generalized Moments Estimation for Panel Data / Viliam Druska, William C. Horrace.

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Druska, Viliam

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TextSeries: Technical Working Paper Series (National Bureau of Economic Research) ; no. t0291.Publication details: Cambridge, Mass. National Bureau of Economic Research 2003.Description: 1 online resource: illustrations (black and white)Subject(s):

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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.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber t0291 (Browse shelf(Opens below))	Not For Loan

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March 2003.

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.