Abstract
Likelihood-based estimation of the normal-gamma stochastic frontier model requires numerical integration to solve its likelihood. For the integration methods found in the literature, it is not known under which conditions they perform optimally or if there is a method that performs better than the others. Our aim is to study the applicability of available methods and to compare them based on their ability to approximate the loglikelihood. We consider three principles—numerical quadrature, inversion of the characteristic function and Monte Carlo—and assess the effect of the parameters on the accuracy of each of six numerical procedures.
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Notes
SF analysis was preceded by data envelopment analysis and (non-stochastic) frontier models. A survey of related methods can be found in the work edited by Fried et al. (2008).
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Acknowledgements
GSS is grateful to the National Council for Scientific and Technological Development—CNPq/Brazil, for financial support. BBA has been partially funded by the Federal District Research Foundation, FAP/DF.
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de Andrade, B.B., Souza, G.S. Likelihood computation in the normal-gamma stochastic frontier model. Comput Stat 33, 967–982 (2018). https://doi.org/10.1007/s00180-017-0768-5
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DOI: https://doi.org/10.1007/s00180-017-0768-5