On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model : some new formulations and generalizations

Kumbhakar, Subal C. and Tsionas, Mike G. (2020) On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model : some new formulations and generalizations. European Journal of Operational Research, 287 (2). pp. 762-775. ISSN 0377-2217

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Abstract

In this paper we propose some alternative formulations and estimation of technical and allocative inefficiency in the presence of some exogenous variables in the context of a panel stochastic frontier model which includes time-invariant firm effects (heterogeneity) along with time-varying technical inefficiency and random noise. These exogenous variables are used to explain technical and allocative inefficiency as well as firm heterogeneity. The presence of these exogenous variables allows us to relax some of the assumptions made in a recent paper by Lai and Kumbhakar (2019). These variables also allow to add flexibility in estimating the model parameters as well as both technical and allocative inefficiency and costs therefrom. More specifically, the incidental parameters problem associated with firm heterogeneity in the production function as well in the first-order conditions of cost minimization can be avoided by parameterizing them in terms of the exogenous variables. We propose and implement model comparison based on Bayes factors and marginal likelihood.

Item Type:
Journal Article
Journal or Publication Title:
European Journal of Operational Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2611
Subjects:
?? productivity and competitivenessendogeneitytechnical efficiencyallocative efficiencyincidental parameters problemmodelling and simulationmanagement science and operations researchinformation systems and management ??
ID Code:
149576
Deposited By:
Deposited On:
02 Dec 2020 11:55
Refereed?:
Yes
Published?:
Published
Last Modified:
04 Sep 2024 00:09