Stochastic frontier analysis using Stata
Federico Belotti
Centre for Economic and International Studies
University of Rome Tor Vergata
Rome, Italy
[email protected]
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Silvio Daidone
Centre for Health Economics
University of York
York, UK
[email protected]
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Giuseppe Ilardi
Economic and Financial Statistics Department
Bank of Italy
Rome, Italy
[email protected]
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Vincenzo Atella
Centre for Economic and International Studies
University of Rome Tor Vergata
Rome, Italy
[email protected]
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Abstract. This article describes sfcross and sfpanel, two new Stata
commands for the estimation of cross-sectional and panel-data stochastic
frontier models. sfcross extends the capabilities of the frontier
command by including additional models (Greene, 2003, Journal of Productivity Analysis 19: 179–190; Wang, 2002, Journal of Productivity Analysis 18: 241–253) and command functionality, such
as the possibility of managing complex survey data characteristics.
Similarly, sfpanel allows one to fit a much wider range of time-varying inefficiency models compared with the xtfrontier command, including the model of Cornwell, Schmidt, and Sickles (1990, Journal of Econometrics 46: 185–200); the model of Lee and Schmidt (1993, in The Measurement of Productive Efficiency: Techniques and Applications), a production frontier model with flexible temporal variation in technical efficiency; the flexible model of Kumbhakar (1990, Journal of Econometrics 46: 201–211); the inefficiency effects model of Battese and Coelli (1995 Empirical Economics 20: 325–332); and the "true" fixed- and random-effects models of Greene (2005a, Journal of Econometrics 126: 269–303). A brief overview of the stochastic frontier literature, a description of the two commands and their options,
and examples using simulated and real data are provided.
View all articles by these authors:
Federico Belotti, Silvio Daidone, Giuseppe Ilardi, Vincenzo Atella
View all articles with these keywords:
sfcross, sfpanel, stochastic frontier analysis, production frontier, cost frontier, cross-sectional, panel data
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