Estimating the Characteristics of Polluting Technologies
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Polluting technologies can be represented using output distance functions. A common approach to estimating such functions is to factor out
one of the outputs and estimate the resulting equation using well-known
stochastic frontier estimation methods, including maximum likelihood. A
problem with this approach is that the outputs that are not factored out
may be correlated with the error term, leading to biased and inconsistent
estimates. This paper addresses the problem in a Bayesian framework.
The methodology is applied to data on U.S. electric utilities. Results
include estimates of technical inefficiencies and the shadow price of a pollutant.