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

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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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 231.

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Length: 29 pages
Date of creation: 18 Apr 2012
Date of revision: 18 Apr 2012
Handle: RePEc:rtv:ceisrp:231
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  1. 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.
  2. 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.
  3. Wang, Hung-jen & Schmidt, Peter, 2001. "One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels," MPRA Paper 31075, University Library of Munich, Germany, revised Mar 2002.
  4. Poirier, Dale J & Ruud, Paul A, 1988. "Probit with Dependent Observations," Review of Economic Studies, Wiley Blackwell, vol. 55(4), pages 593-614, October.
  5. Ritter, C. & Simar, L., . "Pitfalls of normal-gamma stochastic frontier models," CORE Discussion Papers RP -1272, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. 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.
  7. 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.
  8. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
  9. Wang, Hung-Jen, 2006. "Stochastic frontier models," MPRA Paper 31079, University Library of Munich, Germany.
  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. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Wei Siang Wang & Peter Schmidt, 2007. "On The Distribution of Estimated Technical Efficiency in Stochastic Frontier Models," CEPA Working Papers Series WP022007, School of Economics, University of Queensland, Australia.
  16. 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.
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