Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function
By William H. Greene. 2000
181 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/602ea05b396703c46cacb054119194febe9357a3
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
- Giuseppe Ilardi
- Federico Belotti
- William H. Greene
- Efthymios G. Tsionas
- Murray D. Smith
- Alexander D. Stead
- Phill Wheat
- Massimo Filippini
In Management Science and Operations Research
- Federico Belotti
- Giuseppe Ilardi
- William H. Greene
- Efthymios G. Tsionas
- Murray D. Smith
- Alexander D. Stead
- Phill Wheat
- Massimo Filippini
Citations in the corpus
- Stochastic frontier models with dependent error components (2008, methodology): “7677 of the Frank SFM to the same statistic reported by Greene (1990) for other models fitted to these data, all of which imposed independence amongst the error components.”
- Maximum likelihood estimation of stochastic frontier models by the Fourier transform (2012, methodology): “closed form so Kumbhakar and Tsionas (2004) proposed to approximate it using Monte Carlo methods resulting in the method of MSL.”
- Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England (2019, methodology): “…to this problem is to use simulation to approximate the integral and arrive at a simulated log likelihood function—see Train (2003) for an introduction to maximum simulated likelihood—as proposed by Greene (2003) for the normal-gamma model and Stead et al. (2017) for the logistic-half normal model.”
- Consistent inference in fixed-effects stochastic frontier models (2017, methodology): “As pointed out by Greene (2003), the rule of thumb “the more the better” is not helpful when time (and computational power) becomes a consideration.”
- Reconsidering heterogeneity in panel data estimators of the stochastic frontier model (2005, background): “(See Gourieroux and Monfort (1996) and Greene (2001, 2003b).”
- Measuring environmental efficiency of industry: a case study of thermal power generation in India (2007, unknown): “Greene (2000) shows that the gamma model has the virtue of providing a richer and more flexible parameterization of the inefficiency distribution in the stochastic frontier model than either of the canonical forms, half normal and exponential.”