Maximum Likelihood Estimator for Spatial Stochastic Frontier Models
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.
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- Viliam Druska & William C. Horrace, 2004.
"Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming,"
American Journal of Agricultural Economics,
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- 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.
- Pavlyuk, Dmitry, 2012. "Airport Benchmarking and Spatial Competition: a Critical Review," MPRA Paper 43391, University Library of Munich, Germany.
- repec:cup:cbooks:9780521666633 is not listed on IDEAS
- 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, June.
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