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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26195
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| Title: | Fixed and Random Effects in Stochastic Frontier Models |
| Authors: | Greene, William |
| Keywords: | Panel data
fixed effects
random effects
random parameters
computation
Monte Carlo
maximum simulated likelihood
technical efficiency
stochastic frontier |
| Issue Date: | Oct-2002 |
| Series/Report no.: | EC-02-16 |
| Abstract: | 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. |
| URI: | http://hdl.handle.net/2451/26195 |
| Appears in Collections: | Economics Working Papers
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