Fronteira de Produção Estocástica: Uma Abordagem Bayesiana
Thaís C.O. da Fonseca
The model of stochastic production frontier on the classical specification getsmaximum likelihood estimates of model parameters e agents productivity. Theproposed Bayesian specification, estimated using Monte Carlo Markov Chain(MCMC) gets a sample from the distribution of the parameter and productivityestimator, which make possible measure the expected value and the interval ofmaximum density. In this paper is showed the implementation of this model, and isshowed, empirically, that the maximum likelihood estimator has a bias greater thanthe Bayesian version, in particular for the second moments.
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