Abstract
We propose a stochastic frontier model with random coefficients having a flexible distribution. The distribution is modelled non-parametrically. It is shown that maximum likelihood estimation reduces to a fixed-point problem. A fixed-point iteration is proposed and we show that there is a unique regular fixed point. The fixed-point iteration is used in the context of MCMC to perform inferences for all unknown parameters including the optimal support of the distribution of random coefficients.
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