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Bayesian estimation of the half-normal regression model with deterministic frontier

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Abstract

A regression model with deterministic frontier is considered. This type of model has hardly been studied, partly owing to the difficulty in the application of maximum likelihood estimation since this is a non-regular model. As an alternative, the Bayesian methodology is proposed and analysed. Through the Gibbs algorithm, the inference of the parameters of the model and of the individual efficiencies are relatively straightforward. The results of the simulations indicate that the utilized method performs reasonably well.

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Notes

  1. The parameter \(\gamma \) mentioned is the proportion of the total variance of the composite error due to inefficiency. Therefore, if it is estimated to be 1, then the whole error is inefficiency, that is to say, the model does not have random noise and its frontier is deterministic.

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Authors and Affiliations

  1. Universidad de Sevilla, Avenida Ramón y Cajal 1, Seville, Spain

    Francisco J. Ortega & Jose M. Gavilan

Authors
  1. Francisco J. Ortega
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  2. Jose M. Gavilan
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Correspondence to Jose M. Gavilan.

Appendix: Sets of data utilized in the examples

Appendix: Sets of data utilized in the examples

See Tables 8, 9 and 10.

Table 8 Simulated sample (Example 1)
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Table 9 Simulated sample (Example 2)
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Table 10 Set of data on the production and inputs in the example of telecommunications (Sect. 5)
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Ortega, F.J., Gavilan, J.M. Bayesian estimation of the half-normal regression model with deterministic frontier. Comput Stat 31, 1059–1078 (2016). https://doi.org/10.1007/s00180-016-0648-4

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