Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach

CER-ETH – Center of Economic Research at ETH Zurich Working Paper No. 14/197

24 Pages Posted: 24 May 2014

See all articles by Massimo Filippini

Massimo Filippini

ETH Zürich; University of Lugano - Faculty of Economics

William H. Greene

New York University Stern School of Business

Date Written: May 22, 2014

Abstract

The productive efficiency of a firm can be seen as composed of two parts, one persistent and one transient. The received empirical literature on the measurement of productive efficiency has paid relatively little attention to the difference between these two components. Ahn, Good and Sickles (2000) suggested some approaches that pointed in this direction. The possibility was also raised in Greene (2004), who expressed some pessimism over the possibility of distinguishing the two empirically. Recently, Colombi (2010) and Kumbhakar and Tsionas (2012), in a milestone extension of the stochastic frontier methodology have proposed a tractable model based on panel data the promises to provide separate estimates of the two components of efficiency. The approach developed in the original presentation proved very cumbersome actually to implement in practice. Colombi (2010) notes that FIML estimation of the model is ‘complex and time consuming.’ In the sequence of papers, Colombi (2010), Colombi et al. (2011, 2014), Kumbhakar, Lien and Hardaker (2012) and Kumbhakar and Tsionas (2012) have suggested other strategies, including a four step least squares method. The main point of this paper is that full maximum likelihood estimation of the model is neither complex nor time consuming. The extreme complexity of the log likelihood noted in Colombi (2010), Colombi et al. (2011, 2014) is reduced by using simulation and exploiting the Butler and Moffitt (1982) formulation. In this paper, we develop a practical full information maximum simulated likelihood estimator for the model. The approach is very effective and strikingly simple to apply, and uses all of the sample distributional information to obtain the estimates. We also implement the panel data counterpart of the JLMS (1982) estimator for technical or cost inefficiency. The technique is applied in a study of the cost efficiency of Swiss railways.

Keywords: productive efficiency, stochastic frontier analysis, panel data, transient and persistent efficiency

JEL Classification: C1, C23, D2, D24

Suggested Citation

Filippini, Massimo and Greene, William H., Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach (May 22, 2014). CER-ETH – Center of Economic Research at ETH Zurich Working Paper No. 14/197, Available at SSRN: https://ssrn.com/abstract=2440704 or http://dx.doi.org/10.2139/ssrn.2440704

Massimo Filippini (Contact Author)

ETH Zürich ( email )

ETH-Zentrum
CH-8092 Zurich
Switzerland

University of Lugano - Faculty of Economics ( email )

Via Giuseppe Buffi 13
CH-6900 Lugano, CH-6904
Switzerland

William H. Greene

New York University Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States
212-998-0876 (Phone)

HOME PAGE: http://people.stern.nyu.edu/wgreene

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