On the distribution of estimated technical efficiency in stochastic frontier models
We consider a stochastic frontier model with error [epsilon]=v-u, where v is normal and u is half normal. We derive the distribution of the usual estimate of u,E(u[epsilon]). We show that as the variance of v approaches zero, E(u[epsilon])-u converges to zero, while as the variance of v approaches infinity, E(u[epsilon]) converges to E(u). We graph the density of E(u[epsilon]) for intermediate cases. To show that E(u[epsilon]) is a shrinkage of u towards its mean, we derive and graph the distribution of E(u[epsilon]) conditional on u. We also consider the distribution of estimated inefficiency in the fixed-effects panel data setting.
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