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Consistent Estimation of the “True” Fixed-effects Stochastic Frontier Model

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The classic stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a,b) proposed a fixed-effects model specification that distinguishes these two latent components and allows a time varying inefficiency distribution. However, the maximum likelihood estimator proposed by Greene leads to biased inefficiency estimates due to the incidental parameters problem. In this paper, we propose two alternative estimation procedures that, by relying on a first difference data transformation, achieve consistency for n goes to infinity with fixed T. Evidence from Monte Carlo simulations shows good finite sample performances of both approaches even in presence of small samples.

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  • Federico Belotti & Giuseppe Ilardi, 2012. "Consistent Estimation of the “True” Fixed-effects Stochastic Frontier Model," CEIS Research Paper 231, Tor Vergata University, CEIS, revised 18 Apr 2012.
  • Handle: RePEc:rtv:ceisrp:231
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    1. Christian Ritter & Léopold Simar, 1997. "Pitfalls of Normal-Gamma Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 8(2), pages 167-182, May.
    2. Dale J. Poirier & Paul A. Ruud, 1988. "Probit with Dependent Observations," Review of Economic Studies, Oxford University Press, vol. 55(4), pages 593-614.
    3. Wang, Wei Siang & Schmidt, Peter, 2009. "On the distribution of estimated technical efficiency in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 148(1), pages 36-45, January.
    4. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    5. Gian Paolo Barbetta & Gilberto Turati & Angelo M. Zago, 2007. "Behavioral differences between public and private not-for-profit hospitals in the Italian national health service," Health Economics, John Wiley & Sons, Ltd., vol. 16(1), pages 75-96.
    6. Silvio Daidone & Francesco D’Amico, 2009. "Technical efficiency, specialization and ownership form: evidences from a pooling of Italian hospitals," Journal of Productivity Analysis, Springer, vol. 32(3), pages 203-216, December.
    7. Wang, Hung-Jen, 2006. "Stochastic frontier models," MPRA Paper 31079, University Library of Munich, Germany.
    8. Honore, Bo E. & Powell, James L., 1994. "Pairwise difference estimators of censored and truncated regression models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 241-278.
    9. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    10. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
    11. 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.
    12. Carlos Martins-Filho & Feng Yao, 2010. "A note on some properties of a skew-normal density," Working Papers 10-10, Department of Economics, West Virginia University.
    13. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
    14. Andres Aradillas-Lopez & Bo E. Honoré & James L. Powell, 2007. "Pairwise Difference Estimation With Nonparametric Control Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1119-1158, November.
    15. Abrevaya, Jason, 1999. "Leapfrog estimation of a fixed-effects model with unknown transformation of the dependent variable," Journal of Econometrics, Elsevier, vol. 93(2), pages 203-228, December.
    16. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Marco Committeri & Carola Pessino, 2013. "Understanding Countries’ Tax Effort," IMF Working Papers 13/244, International Monetary Fund.
    2. repec:bla:jageco:v:68:y:2017:i:2:p:494-517 is not listed on IDEAS
    3. Giovanni Marin & Alessandro Palma, 2015. "Technology Invention and Diffusion in Residential Energy Consumption. A Stochastic Frontier Approach," Working Papers 2015.104, Fondazione Eni Enrico Mattei.
    4. Alejandro U. Becerra Ornelas & Héctor M. Núñez, 2017. "Stochastic Frontiers and Technical Efficiency of Local Public Expenditure in Mexico," Working papers DTE 603, CIDE, División de Economía.
    5. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
    6. repec:eee:eneeco:v:66:y:2017:i:c:p:85-98 is not listed on IDEAS
    7. Yuda, Michio, 2016. "Inefficiencies in the Japanese National Health Insurance system: A stochastic frontier approach," Journal of Asian Economics, Elsevier, vol. 42(C), pages 65-77.
    8. Ferro, Gustavo & Lentini, Emilio J. & Mercadier, Augusto C. & Romero, Carlos A., 2014. "Efficiency in Brazil's water and sanitation sector and its relationship with regional provision, property and the independence of operators," Utilities Policy, Elsevier, vol. 28(C), pages 42-51.
    9. Ipatova, Irina & Peresetsky, Аnatoly, 2013. "Technical efficiency of Russian plastic and rubber production firms," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 32(4), pages 71-92.

    More about this item

    Keywords

    Stochastic frontiers; Fixed-effects; Panel data; Marginal simulated likelihood; Pairwise differencing;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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