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Estimating Monotone Concave Stochastic Production Frontiers

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Estimating Monotone Concave Stochastic Production Frontiers. / Tsionas, Mike.
In: Journal of Business and Economic Statistics, Vol. 40, No. 3, 30.06.2022, p. 1403-1414.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, M 2022, 'Estimating Monotone Concave Stochastic Production Frontiers', Journal of Business and Economic Statistics, vol. 40, no. 3, pp. 1403-1414. https://doi.org/10.1080/07350015.2021.1931240

APA

Tsionas, M. (2022). Estimating Monotone Concave Stochastic Production Frontiers. Journal of Business and Economic Statistics, 40(3), 1403-1414. https://doi.org/10.1080/07350015.2021.1931240

Vancouver

Tsionas M. Estimating Monotone Concave Stochastic Production Frontiers. Journal of Business and Economic Statistics. 2022 Jun 30;40(3):1403-1414. Epub 2021 Jun 30. doi: 10.1080/07350015.2021.1931240

Author

Tsionas, Mike. / Estimating Monotone Concave Stochastic Production Frontiers. In: Journal of Business and Economic Statistics. 2022 ; Vol. 40, No. 3. pp. 1403-1414.

Bibtex

@article{6ec8b42211fb448098f963daf262d48c,
title = "Estimating Monotone Concave Stochastic Production Frontiers",
abstract = "Recent research shows that the search for Bayesian estimation of concave production functions is a fruitful area of investigation. In this article, we use a flexible cost function that satisfies globally the monotonicity and curvature properties to estimate features of the production function. Specification of a globally monotone concave production function is a difficult task which is avoided here by using the first-order conditions for cost minimization from a globally monotone concave cost function. The problem of unavailable factor prices is bypassed by assuming structure for relative prices in the first-order conditions. The new technique is shown to perform well in a Monte Carlo experiment as well as in an empirical application to rice farming in India.",
keywords = "Bayesian analysis, Optimization, Productivity and competitiveness, Stochastic frontiers",
author = "Mike Tsionas",
year = "2022",
month = jun,
day = "30",
doi = "10.1080/07350015.2021.1931240",
language = "English",
volume = "40",
pages = "1403--1414",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "American Statistical Association",
number = "3",

}

RIS

TY - JOUR

T1 - Estimating Monotone Concave Stochastic Production Frontiers

AU - Tsionas, Mike

PY - 2022/6/30

Y1 - 2022/6/30

N2 - Recent research shows that the search for Bayesian estimation of concave production functions is a fruitful area of investigation. In this article, we use a flexible cost function that satisfies globally the monotonicity and curvature properties to estimate features of the production function. Specification of a globally monotone concave production function is a difficult task which is avoided here by using the first-order conditions for cost minimization from a globally monotone concave cost function. The problem of unavailable factor prices is bypassed by assuming structure for relative prices in the first-order conditions. The new technique is shown to perform well in a Monte Carlo experiment as well as in an empirical application to rice farming in India.

AB - Recent research shows that the search for Bayesian estimation of concave production functions is a fruitful area of investigation. In this article, we use a flexible cost function that satisfies globally the monotonicity and curvature properties to estimate features of the production function. Specification of a globally monotone concave production function is a difficult task which is avoided here by using the first-order conditions for cost minimization from a globally monotone concave cost function. The problem of unavailable factor prices is bypassed by assuming structure for relative prices in the first-order conditions. The new technique is shown to perform well in a Monte Carlo experiment as well as in an empirical application to rice farming in India.

KW - Bayesian analysis

KW - Optimization

KW - Productivity and competitiveness

KW - Stochastic frontiers

U2 - 10.1080/07350015.2021.1931240

DO - 10.1080/07350015.2021.1931240

M3 - Journal article

VL - 40

SP - 1403

EP - 1414

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

IS - 3

ER -