Generalized Moments Estimation for Panel Data
AbstractThis 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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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0291.
Date of creation: Mar 2003
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Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-04-02 (All new papers)
- NEP-ECM-2003-04-04 (Econometrics)
- NEP-ETS-2003-04-02 (Econometric Time Series)
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