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Fixed and Random Effects in Stochastic Frontier Models

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  • William Greene

Abstract

Received stochastic frontier analyses with panel data have relied on traditional fixed and random effects models. We propose extensions that circumvent two shortcomings of these approaches. The conventional panel data estimators assume that technical or cost inefficiency is time invariant. Second, the fixed and random effects estimators force any time invariant cross unit heterogeneity into the same term that is being used to capture the inefficiency. Inefficiency measures in these models may be picking up heterogeneity in addition to or even instead of inefficiency. A fixed effects model is extended to the stochastic frontier model using results that specifically employ the nonlinear specification. The random effects model is reformulated as a special case of the random parameters model. The techniques are illustrated in applications to the U.S. banking industry and a cross country comparison of the efficiency of health care delivery. Copyright Springer Science+Business Media, Inc. 2005

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Bibliographic Info

Paper provided by New York University, Leonard N. Stern School of Business, Department of Economics in its series Working Papers with number 02-16.

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Date of creation: 2002
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Handle: RePEc:ste:nystbu:02-16

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Postal: New York University, Leonard N. Stern School of Business, Department of Economics, 44 West 4th Street, New York, NY 10012-1126
Phone: (212) 998-0860
Fax: (212) 995-4218
Web page: http://w4.stern.nyu.edu/economics/
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  1. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
  2. Greene, W., 2001. "Fixed and Random Effects in Nonlinear Models," New York University, Leonard N. Stern School Finance Department Working Paper Seires 01-01, New York University, Leonard N. Stern School of Business-.
  3. William Greene, 2002. "The Behavior of the Fixed Effects Estimator in Nonlinear Models," Working Papers 02-05, New York University, Leonard N. Stern School of Business, Department of Economics.
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