Stochastic Frontier Models Using GAUSS
This paper discusses the use of ten different GAUSS programs for various stochastic frontier models. SFM_MLE_cross-section provides maximum likelihood estimates (MLE) for four different stochastic frontier models with cross-sectional data: those of Aigner, Lovell, and Schmidt (1977), Stevenson (1980), Almanidis, Qian, and Sickles (2014), and Lee and Lee (2014). There are two programs for panel data stochastic frontier models with the time-invariant efficiency assumption. SFM_BC88_MLE provides the MLE of Battese and Coelli (1988) and SFM_SS presents the within and generalized least squared estimates of Schmidt and Sickles (1984). Finally, seven programs allow the use of different stochastic frontier models with time-varying efficiency: SFM_BC92 for Battese and Coelli (1992), SFM_Kum for Kumbhakar (1991), SFM_CSS for Cornwell, Schmidt, and Sickles (1990), SFM_LS for Lee and Schmidt (1993), SFM_GrLS for Lee (2006), SFM_GrBC for Lee (2010), and SFM_ALS07 for Ahn, Lee, and Schmidt (2007). A noteworthy feature is that all seven programs estimate production function parameters by adopting the fixed effect treatment.
|Date of creation:||2014|
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Journal of Econometrics,
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