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Bayesian estimation of inefficiency heterogeneity in stochastic frontier models

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  • Wiper, Michael Peter
  • Lopes Moreira Da Veiga, María Helena
  • Galán Camacho, Jorge Eduardo

Abstract

Estimation of the one sided error component in stochastic frontier models may erroneously attribute firm characteristics to inefficiency if heterogeneity is unaccounted for. However, it is not clear in general in which component of the error distribution the covariates should be included. In the classical context, some studies include covariates in the scale parameter of the inefficiency with the property of preserving the shape of its distribution. We extend this idea to Bayesian inference for stochastic frontier models capturing both observed and unobserved heterogeneity under half normal, truncated and exponential distributed inefficiencies. We use the WinBugs package to implement our approach throughout. Our findings using two real data sets, illustrate the relevant effects on shrinking and separating individual posterior efficiencies when heterogeneity affects the scale of the inefficiency. We also see that the inclusion of unobserved heterogeneity is still relevant when no observable covariates are available.

Suggested Citation

  • Wiper, Michael Peter & Lopes Moreira Da Veiga, María Helena & Galán Camacho, Jorge Eduardo, 2012. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," DES - Working Papers. Statistics and Econometrics. WS ws121007, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws121007
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    Citations

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    Cited by:

    1. Galán, Jorge E. & Pollitt, Michael G., 2014. "Inefficiency persistence and heterogeneity in Colombian electricity utilities," Energy Economics, Elsevier, vol. 46(C), pages 31-44.
    2. Wiper, Michael Peter & Lopes Moreira Da Veiga, María Helena & Deng, Yaguo, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Sarmiento, Miguel & Galán, Jorge E., 2017. "The influence of risk-taking on bank efficiency: Evidence from Colombia," Emerging Markets Review, Elsevier, vol. 32(C), pages 52-73.
    4. Wiper, Michael Peter & Lopes Moreira Da Veiga, María Helena & Galán Camacho, Jorge Eduardo, 2013. "Bayesian analysis of dynamic effects in inefficiency : evidence from the Colombian banking sector," DES - Working Papers. Statistics and Econometrics. WS ws131918, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. repec:kap:jproda:v:49:y:2018:i:2:d:10.1007_s11123-018-0529-7 is not listed on IDEAS
    6. Galán, Jorge E. & Sarmiento, Miguel, 2014. "Heterogeneous effects of risk-taking on bank efficiency : a stochastic frontier model with random coefficients," DES - Working Papers. Statistics and Econometrics. WS ws142013, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. repec:eee:tefoso:v:126:y:2018:i:c:p:186-193 is not listed on IDEAS
    8. Baños, José F. & Rodríguez-Álvarez, Ana & Suárez, Patricia, 2016. "Matching frontiers: A random parameter model approach," Efficiency Series Papers 2016/07, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    9. Cinzia Daraio, 2017. "A framework for the Assessment of Research and its impacts," DIAG Technical Reports 2017-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    10. Hampf, Benjamin, 2015. "Estimating the materials balance condition: A stochastic frontier approach," Darmstadt Discussion Papers in Economics 226, Darmstadt University of Technology, Department of Law and Economics.
    11. Jorge E. Galán & Michael G. Pollitt, 2014. "Inefficiency persistence and heterogeneity in Colombian electricity distribution utilities," Cambridge Working Papers in Economics 1423, Faculty of Economics, University of Cambridge.

    More about this item

    Keywords

    Stochastic Frontier Models;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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