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Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming

  • Viliam Druska

    (Charles Univeristy)

  • William C. Horrace

    (Syracuse University)

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.

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File URL: http://econwpa.repec.org/eps/em/papers/0206/0206004.pdf
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Paper provided by EconWPA in its series Econometrics with number 0206004.

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Length: 37 pages
Date of creation: 19 Jun 2002
Date of revision: 11 May 2003
Handle: RePEc:wpa:wuwpem:0206004
Note: Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP; pages: 37; figures: included. Spatial GMM for panel data applied to a stochastic frontier model
Contact details of provider: Web page: http://econwpa.repec.org

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  1. Hausman, Jerry A. & Taylor, William E., 1981. "Panel data and unobservable individual effects," Journal of Econometrics, Elsevier, vol. 16(1), pages 155-155, May.
  2. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  3. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
  4. J. Bradford De Long & Lawrence H. Summers, 1990. "Equipment Investment and Economic Growth," NBER Working Papers 3515, National Bureau of Economic Research, Inc.
  5. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  6. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
  7. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-38, May.
  8. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1989. "Production Frontiers With Cross-Sectinal And Time-Series Variation In Efficiency Levels," Working Papers 89-18, C.V. Starr Center for Applied Economics, New York University.
  9. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
  10. William C. Horrace & Peter Schmidt, 2000. "Multiple comparisons with the best, with economic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-26.
  11. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
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