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Stochastic frontiers with a Rayleigh distribution

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  • Gholamreza Hajargasht

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Abstract

We introduce a stochastic frontier model with a one-parameter distribution known as the Rayleigh distribution which has a non-zero mode and yet it is easy to estimate and use. We show how this model can be estimated using various estimation methods. The Rayleigh model with environmental variables and time-varying features is also considered. It is also tested against exponential and half-normal models using two real data sets. Copyright Springer Science+Business Media New York 2015

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  • Gholamreza Hajargasht, 2015. "Stochastic frontiers with a Rayleigh distribution," Journal of Productivity Analysis, Springer, vol. 44(2), pages 199-208, October.
  • Handle: RePEc:kap:jproda:v:44:y:2015:i:2:p:199-208
    DOI: 10.1007/s11123-014-0417-8
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    References listed on IDEAS

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    Cited by:

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    2. Phill Wheat & Alexander D. Stead & William H. Greene, 2019. "Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England," Journal of Productivity Analysis, Springer, vol. 51(1), pages 21-38, February.
    3. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.

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    More about this item

    Keywords

    Technical efficiency; Exponential; Half-normal; C13; C23; C33;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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