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Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency

Federico Belotti (), Giuseppe Ilardi and Andrea Piano Mortari ()

No 459, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: This paper proposes a stochastic frontier panel data model in which unit-specific inefficiencies are spatially correlated. In particular, this model has simultaneously three important features: i) the total inefficiency of a productive unit depends on its own inefficiency and on the inefficiency of its neighbors; ii) the spatially correlated and time varying inefficiency is disentangled from time invariant unobserved heterogeneity in a panel data model à la Greene (2005); iii) systematic differences in inefficiency can be explained using exogenous determinants. We propose to estimate both the "true" fixed- and random-effects variants of the model using a feasible simulated composite maximum likelihood approach. The finite sample behavior of the proposed estimators are investigated through a set of Monte Carlo experiments. Our simulation results suggest that the estimation approach is consistent, showing good finite sample properties especially in small samples.

Keywords: Stochastic frontiers model; Spatial inefficiency; Panel data, Fixed-effects model (search for similar items in EconPapers)
JEL-codes: C13 C23 C15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
Date: 2019-05-30, Revised 2019-05-30
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