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Maximum Likelihood Estimator for Spatial Stochastic Frontier Models

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  • Pavlyuk, Dmitry

Abstract

This research is devoted to analysis of efficiency estimation in presence of spatial relationships and spatial heterogeneity in data. We presented a general specification of the spatial stochastic frontier model, which includes spatial lags, spatial autoregressive disturbances and spatial autoregressive inefficiencies. Maximum likelihood estimators are derived for two special cases of the spatial stochastic frontier. Small-sample properties of these estimators and comparison with a standard non-spatial estimator were implemented using a set of Monte Carlo experiments. Finally, we tested our estimators on a real-world data set of European airports and discovered significant spatial components in data.

Suggested Citation

  • Pavlyuk, Dmitry, 2012. "Maximum Likelihood Estimator for Spatial Stochastic Frontier Models," MPRA Paper 43390, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:43390
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    References listed on IDEAS

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    1. Masahisa Fujita & Paul Krugman & Anthony J. Venables, 2001. "The Spatial Economy: Cities, Regions, and International Trade," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262561476, December.
    2. Pavlyuk, Dmitry, 2012. "Airport Benchmarking and Spatial Competition: a Critical Review," MPRA Paper 43391, University Library of Munich, Germany.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    4. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    5. Viliam Druska & William C. Horrace, 2004. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 185-198.
    6. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    7. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    8. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    9. 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.
    10. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, September.
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    Cited by:

    1. Jacopo Canello & Francesco Vidoli, 2020. "Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 653-676, September.
    2. Fusco, Elisa & Allegrini, Veronica, 2020. "The role of spatial interdependence in local government cost efficiency: An application to waste Italian sector," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    3. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.
    4. Jaepil Han & Deockhyun Ryu & Robin Sickles, 2016. "How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model," Advances in Econometrics, in: Spatial Econometrics: Qualitative and Limited Dependent Variables, volume 37, pages 259-294, Emerald Group Publishing Limited.
    5. Vidoli, Francesco & Cardillo, Concetta & Fusco, Elisa & Canello, Jacopo, 2016. "Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry," Regional Science and Urban Economics, Elsevier, vol. 61(C), pages 153-164.

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    Keywords

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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