Probabilistic frontier regression models for binary type output data

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as 'selected' category and the reduction in probability of falling in this category is attributed to the reduction in te...

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Bibliographic Details
Published in:Journal of applied statistics 2019-10, Vol.46 (13), p.2460-2480
Main Authors: Badade, Meena, Ramanathan, T. V.
Format: Article
Language:English
Subjects:
Binary type output data
Computer simulation
Decision making
probabilistic frontier regression models
Public sector
Reduction
Regression models
Statistical analysis
Statistical methods
technical efficiency
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Summary:This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as 'selected' category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2019.1597838