Fixed and Random Effects in Stochastic Frontier Models

45 Pages Posted: 31 Oct 2008

See all articles by William H. Greene

William H. Greene

New York University Stern School of Business

Date Written: October 2002


Received analyses based on stochastic frontier modeling with panel data have relied primarily on results from traditional linear fixed and random effects models. This paper examines extensions of these models that circumvent two important shortcomings of the existing fixed and random effects approaches. The conventional panel data stochastic frontier estimators both assume that technical or cost inefficiency is time invariant. In a lengthy panel, this is likely to be a particularly strong assumption. Second, as conventionally formulated, 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. Thus, measures of inefficiency in these models may be picking up heterogeneity in addition to or even instead of technical or cost inefficiency. In this paper, a true 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 that retains the fundamental structure of the stochastic frontier model. The techniques are illustrated through two applications, a large panel from the U.S. banking industry and a cross country comparison of the efficiency of health care delivery.

Keywords: Panel data, fixed effects, random effects, random parameters, computation, Monte Carlo, maximum simulated likelihood, technical efficiency, stochastic frontier

Suggested Citation

Greene, William H., Fixed and Random Effects in Stochastic Frontier Models (October 2002). NYU Working Paper No. EC-02-16, Available at SSRN:

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