Reconsidering heterogeneity in panel data estimators of the stochastic frontier model

By William H. Greene. 2005

1,718 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b2668ae4ca0e33c60efab941f48862c7d980ac3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Massimo Filippini
2. William H. Greene
3. Subal C. Kumbhakar
4. Federico Belotti
5. Giuseppe Ilardi
6. Timo Kuosmanen
7. L. Hunt
8. J. Zorić

In Management Science and Operations Research

1. Massimo Filippini
2. Subal C. Kumbhakar
3. William H. Greene
4. Federico Belotti
5. Giuseppe Ilardi
6. Timo Kuosmanen
7. Dennis Hilgers
8. Hannes W. Lampe

Citations in the corpus

• Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities (2004, background): “10 See Kumbhakar and Lovell (2000) for a review and Greene (forthcoming) and Tsionas”
• Is There a Single Frontier in a Single European Banking Market? (2007, background): “This can be the result of using a fixed effects estimator (see Greene, 2005, for a discussion).”
• Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach (2011, methodology): “Given this discussion, the “pooled” model based on Aigner et al. (1977) and the True Random Effects (TRE) model proposed by Greene (2005) are used for the estimation of equation (2).”
• Consistent estimation of the fixed effects stochastic frontier model (2014, methodology): “This model has been considered by Greene (2005A, 2005B).”
• Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints (2012, methodology): “3 (2008) also apply DEA to the fitted values of the Kumbhakar et al. (2007) method in order to impose monotonicity and concavity.”
• Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies (2004, methodology): “In order to distinguish external heterogeneities from cost efficiency, Greene (2005, 2004) proposed an approach that integrates an additional stochastic term representing inefficiency in both fixed and random effects models.”
• Can China’s Energy Intensity Constraint Policy Promote Total Factor Energy Efficiency? Evidence from the Industrial Sector (2019, methodology): “Such a fixed-effect SFA model can capture the individual effect reflecting the heterogeneity to avoid biased estimation results (Greene, 2005; Wang and Ho, 2010).”
• Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods (2008, methodology): “2 For example, see Bauer et al. (1998), Estache, Rossi and Ruzzier (2004), Jamasb and Pollitt (2003), Farsi, Filippini and Greene (2005), Farsi and Filippini (2006).”
• Regional growth and finance in Europe : Is there a quality effect of bank efficiency? (2009, methodology): “2) is estimated with maximum likelihood methods as a fixed-effect panel frontier (Greene, 2005).”
• US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach (2012, background): “In this way, the TREM arguably overcomes some of the limitations of conventional frontier panel data models (see Greene, 2005a and 2005b); however, it produces efficiency estimates that do not include the persistent inefficiencies that might remain more or less constant over time.”