Estimation of Dynamic Stochastic Frontier Model using Likelihood-based Approaches

Hung-pin Lai and Subal Kumbhakar

MPRA Paper from University Library of Munich, Germany

Abstract: Almost all the existing panel stochastic frontier models treat technical efficiency as static. Consequently there is no mechanism by which an inefficient producer can improve its efficiency over time. The main objective of this paper is to propose a panel stochastic frontier model that allows the dynamic adjustment of persistent technical inefficiency. The model also includes transient inefficiency which is assumed to be heteroscedastic. We consider three likelihood-based approaches to estimate the model: the full maximum likelihood (FML), pairwise composite likelihood (PCL) and quasi-maximum likelihood (QML) approaches. Moreover, we provide Monte Carlo simulation results to examine and compare the finite sample performances of the three above-mentioned likelihood-based estimators. Finally, we provide an empirical application to the dynamic model.

Keywords: Technical inefficiency; panel data; copula; full maximum likelihood estimation; pairwise composite likelihood estimation; quasi-maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C23 C24 C51 (search for similar items in EconPapers)
Date: 2018-04-10
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
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Related works:
Journal Article: Estimation of a dynamic stochastic frontier model using likelihood‐based approaches (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:87830

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