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A Starting Note: Panel Stochastic Frontier Analysis with Dependent Error Terms

Author

Listed:
  • Rachida El Mehdi

    (SmartICT Lab, National School of Applied Sciences, Mohammed First University.)

  • Christian M. Hafner

    (Louvain Institute of Data Analysis and Modelling in Economics and Statistics, and ISBA, Universit´e catholique de Louvain.)

Abstract

In presence of panel data, technical efficiency is used to compare the performances of Decision-Making Units (DMUs). The novelty of this paper is the consideration of the dependence between the two error terms in the case of panel data and the introduction of time effect models in the Stochastic Frontier Analysis (SFA). Hence, our SFA model considers the balanced panel case, several models describing the evolution of the inefficiency over time and the dependence between the two error terms. The inefficiency and noise terms being dependent, a copula function which reflects the dependence between them is included in their joint density. The model is estimated by maximum likelihood and the Akaike Information Criterion (AIC) is used for model selection. Moreover, a likelihood ratio test is performed for the nested models. A bootstrap algorithm is proposed for statistical inference on the Technical Efficiency (TE) measures. Results for Moroccan policy of the production and sales of drinking water from 2001 to 2007 identify the most and least efficient provinces, and a generally positive trend of estimated TE measures.

Suggested Citation

  • Rachida El Mehdi & Christian M. Hafner, 2021. "A Starting Note: Panel Stochastic Frontier Analysis with Dependent Error Terms," International Econometric Review (IER), Econometric Research Association, vol. 13(2), pages 24-40, June.
  • Handle: RePEc:erh:journl:v:13:y:2021:i:2:p:24-40
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    References listed on IDEAS

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    1. Bhat, Chandra R. & Eluru, Naveen, 2009. "A copula-based approach to accommodate residential self-selection effects in travel behavior modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 749-765, August.
    2. Sangho Kim & Young Hoon Lee, 2006. "The productivity debate of East Asia revisited: a stochastic frontier approach," Applied Economics, Taylor & Francis Journals, vol. 38(14), pages 1697-1706.
    3. Murray D. Smith, 2008. "Stochastic frontier models with dependent error components," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 172-192, March.
    4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    5. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    6. A. Ronald Gallant, 1984. "The Fourier Flexible Form," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(2), pages 204-208.
    7. Tupper, Henrique Cesar & Resende, Marcelo, 2004. "Efficiency and regulatory issues in the Brazilian water and sewage sector: an empirical study," Utilities Policy, Elsevier, vol. 12(1), pages 29-40, March.
    8. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    9. Leopold Simar & Paul Wilson, 2010. "Inferences from Cross-Sectional, Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 62-98.
    10. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    11. Tito Belchior Moreira & Geraldo da Silva Souza & Ricardo Coelho Faria, 2005. "Public Versus Private Water Utilities: Empirical Evidence for Brazilian Companies," Economics Bulletin, AccessEcon, vol. 8(2), pages 1-7.
    12. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    13. repec:ebl:ecbull:v:8:y:2005:i:2:p:1-7 is not listed on IDEAS
    14. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    15. Battese, George E. & Coelli, Tim J. & Colby, T.C., 1989. "Estimation of Frontier Production Functions and the Efficiencies of Indian Farms Using Panel Data from ICRISAT's Village Level Studies," 1989 Conference (33rd), February 7-9, 1989, Christchurch, New Zealand 144383, Australian Agricultural and Resource Economics Society.
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    More about this item

    Keywords

    Bootstrap; Copulas; Efficiency; Panel data; Stochastic frontier analysis;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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