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“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling

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  • Mike G. Tsionas

Abstract

The issues of functional form, distributions of the error components, and endogeneity are for the most part still open in stochastic frontier models. The same is true when it comes to imposition of restrictions of monotonicity and curvature, making efficiency estimation an elusive goal. In this article, we attempt to consider these problems simultaneously and offer practical solutions to the problems raised by Stone and addressed by Badunenko, Henderson and Kumbhakar. We provide major extensions to smoothly mixing regressions and fractional polynomial approximations for both the functional form of the frontier and the structure of inefficiency. Endogeneity is handled, simultaneously, using copulas. We provide detailed computational experiments and an application to U.S. banks. To explore the posteriors of the new models we rely heavily on sequential Monte Carlo techniques.

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  • Mike G. Tsionas, 2017. "“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 948-965, July.
  • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:519:p:948-965
    DOI: 10.1080/01621459.2016.1246364
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    1. Zeebari, Zangin & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2021. "Regularized Conditional Estimators of Unit Inefficiency in Stochastic Frontier Analysis, with Application to Electricity Distribution Market," Ratio Working Papers 345, The Ratio Institute.
    2. Tsionas, Mike G., 2020. "Bounded rationality and thick frontiers in stochastic frontier analysis," European Journal of Operational Research, Elsevier, vol. 284(2), pages 762-768.
    3. Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2019. "Model averaging estimators for the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 51(2), pages 91-103, June.
    4. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2021. "Density deconvolution with Laplace errors and unknown variance," Journal of Productivity Analysis, Springer, vol. 56(2), pages 103-113, December.
    5. Tsionas, Mike G. & Izzeldin, Marwan, 2018. "Smooth approximations to monotone concave functions in production analysis: An alternative to nonparametric concave least squares," European Journal of Operational Research, Elsevier, vol. 271(3), pages 797-807.
    6. Boscan, Luis & Söderberg, Magnus, 2021. "A theoretical and empirical analysis of district heating cost in Denmark," Energy Economics, Elsevier, vol. 99(C).
    7. Lai, Hung-pin & Kumbhakar, Subal C., 2019. "Technical and allocative efficiency in a panel stochastic production frontier system model," European Journal of Operational Research, Elsevier, vol. 278(1), pages 255-265.
    8. Sakouvogui Kekoura & Shaik Saleem & Doetkott Curt & Magel Rhonda, 2021. "Sensitivity analysis of stochastic frontier analysis models," Monte Carlo Methods and Applications, De Gruyter, vol. 27(1), pages 71-90, March.

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