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Export Reference (APA) 
Zheng, H., Ramalho, J. J. S. & Roseta-Palma, C. (2025). Dealing with endogeneity in stochastic frontier models: A comparative assessment of estimators. Energy Economics. 151
Export Reference (IEEE) 
H. Zheng et al.,  "Dealing with endogeneity in stochastic frontier models: A comparative assessment of estimators", in Energy Economics, vol. 151, 2025
Export BibTeX 
@article{zheng2025_1769472468517,
	author = "Zheng, H. and Ramalho, J. J. S. and Roseta-Palma, C.",
	title = "Dealing with endogeneity in stochastic frontier models: A comparative assessment of estimators",
	journal = "Energy Economics",
	year = "2025",
	volume = "151",
	number = "",
	doi = "10.1016/j.eneco.2025.108922",
	url = "https://www.sciencedirect.com/journal/energy-economics"
}
Export RIS 
TY  - JOUR
TI  - Dealing with endogeneity in stochastic frontier models: A comparative assessment of estimators
T2  - Energy Economics
VL  - 151
AU  - Zheng, H.
AU  - Ramalho, J. J. S.
AU  - Roseta-Palma, C.
PY  - 2025
SN  - 0140-9883
DO  - 10.1016/j.eneco.2025.108922
UR  - https://www.sciencedirect.com/journal/energy-economics
AB  - Endogeneity poses a major challenge for Stochastic Frontier Analysis, as input choices may be endogenous to unobserved components of the error term, resulting in biased efficiency estimates. This paper compares leading estimators that address this issue, including control-function estimator (Kutlu, 2010), Generalized Method of Moments (GMM) (Tran and Tsionas, 2013) and copula (Tran and Tsionas, 2015) approaches, as well as the instrumental variable based maximum likelihood estimator (Karakaplan and Kutlu, 2017a,b; Karakaplan, 2022). Monte Carlo simulations reveal distinct bias–variance trade-offs: likelihood-based estimators provide more precise efficiency scores, while GMM and copula can be advantageous in specific contexts. An empirical application to the Portuguese thermal power subsector (2006-2021) shows that accounting for endogeneity significantly alters coefficients and efficiency distributions. These results demonstrate that estimator choice affects policy-relevant indicators such as efficiency scores and determinants of cost performance. Despite data limitations, the study underscores the importance of treating endogeneity and provides methodological guidance for applied efficiency analysis.
ER  -