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Spatial Stochastic Frontier Models: accounting for unobserved local determinants of inefficiency

Author

Listed:
  • Alexandra M. Schmidt
  • Ajax R. B. Moreira
  • Thais C. O. Fonseca
  • Steven M. Helfand

Abstract

In this paper, we analyze the productivity of farms across n = 370 municipalities located in the Center-West region of Brazil. We propose a stochastic frontier model with a latent spatial structure to account for possible unknown geographical variation of the outputs. This spatial component is included in the one-sided disturbance term. We explore two different distributions for this term, the exponential and the truncated normal. We use the Bayesian paradigm to fit the proposed models. We also compare between an independent normal prior and a conditional autoregressive prior for these spatial effects. The inference procedure takes explicit account of the uncertainty when considering these spatial effects. As the resultant posterior distribution does not have a closed form, we make use of stochastic simulation techniques to obtain samples from it. Two different model comparison criteria provide support for the importance of including these latent spatial effects, even after considering covariates at the municipal level.

Suggested Citation

  • Alexandra M. Schmidt & Ajax R. B. Moreira & Thais C. O. Fonseca & Steven M. Helfand, 2006. "Spatial Stochastic Frontier Models: accounting for unobserved local determinants of inefficiency," Discussion Papers 1220, Instituto de Pesquisa Econômica Aplicada - IPEA.
    • Handle: RePEc:ipe:ipetds:1220
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    References listed on IDEAS

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    1. Helfand, Steven M. & Levine, Edward S., 2004. "Farm size and the determinants of productive efficiency in the Brazilian Center-West," Agricultural Economics, Blackwell, vol. 31(2-3), pages 241-249, December.
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    4. Alan Gelfand & Alexandra Schmidt & Sudipto Banerjee & C. Sirmans, 2004. "Nonstationary multivariate process modeling through spatially varying coregionalization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 263-312, December.
    5. Gamerman, Dani & Moreira, Ajax R. B. & Rue, Havard, 2003. "Space-varying regression models: specifications and simulation," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 513-533, March.
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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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