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Probabilistic frontier regression model for multinomial ordinal type output data

Author

Listed:
  • Meena Badade

    (Savitribai Phule Pune University)

  • T. V. Ramanathan

    (Savitribai Phule Pune University)

Abstract

This paper proposes a probabilistic frontier regression model for multinomial ordinal type output data. We consider some of the output categories as ‘categories of interest’ and the reduction in probability of an output falling into these categories is attributed to the lack in technical efficiency (TE) of the decision-making unit. A measure for TE is proposed to determine the deviations of individual units from the probabilistic frontier of ‘categories of interest’. Simulation results show that the average estimated TE is close to its true value. An application of the proposed model is provided to the data related to the Indian companies, where the categorical output variable is an indicator of return on equity (ROE). Individual TE is obtained for each of the decision-making units (companies under consideration).

Suggested Citation

  • Meena Badade & T. V. Ramanathan, 2020. "Probabilistic frontier regression model for multinomial ordinal type output data," Journal of Productivity Analysis, Springer, vol. 53(3), pages 339-354, June.
    • Handle: RePEc:kap:jproda:v:53:y:2020:i:3:d:10.1007_s11123-020-00581-x
    DOI: 10.1007/s11123-020-00581-x
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    Cited by:

    1. Shih-Tang Hwu & Tsu-Tan Fu & Wen-Jen Tsay, 2021. "Estimation and efficiency evaluation of stochastic frontier models with interval dependent variables," Journal of Productivity Analysis, Springer, vol. 56(1), pages 33-44, August.
    2. Meena Badade & T. V. Ramanathan, 2022. "Probabilistic Frontier Regression Models for Count Type Output Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 235-260, September.

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