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A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of U.S. Banks in 2001-2010

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  • Malikov, Emir
  • Kumbhakar, Subal C.
  • Tsionas, Efthymios

Abstract

This paper offers a methodology to address the endogeneity of inputs in the directional technology distance function (DTDF) based formulation of banking technology which explicitly accommodates the presence of undesirable nonperforming loans --- an inherent characteristic of the bank's production due to its exposure to credit risk. Specifically, we model nonperforming loans as an undesirable output in the bank's production process. Since the stochastic DTDF describing banking technology is likely to suffer from the endogeneity of inputs, we propose addressing this problem by considering a system consisting of the DTDF and the first-order conditions from the bank's cost minimization problem. The first-order conditions also allow us to identify the "cost-optimal" directional vector for the banking DTDF, thus eliminating the uncertainty associated with an ad hoc choice of the direction. We apply our cost system approach to the data on large U.S. commercial banks for the 2001-2010 period, which we estimate via Bayesian MCMC methods subject to theoretical regularity conditions. We document dramatic distortions in banks' efficiency, productivity growth and scale elasticity estimates when the endogeneity of inputs is assumed away and/or the DTDF is fitted in an arbitrary direction.

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  • Malikov, Emir & Kumbhakar, Subal C. & Tsionas, Efthymios, 2015. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of U.S. Banks in 2001-2010," MPRA Paper 66490, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:66490
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    Cited by:

    1. Guohua Feng & Chuan Wang & Apostolos Serletis, 2018. "Shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions at US electric utilities: a random-coefficient, random-directional-vector directional output distance function approach," Empirical Economics, Springer, vol. 54(1), pages 231-258, February.
    2. Mike G. Tsionas, 2017. "“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 948-965, July.
    3. Tsionas, Mike G., 2020. "Bounded rationality and thick frontiers in stochastic frontier analysis," European Journal of Operational Research, Elsevier, vol. 284(2), pages 762-768.
    4. Tsionas, Mike G., 2020. "Directional technology distance functions through duality," Economics Letters, Elsevier, vol. 190(C).
    5. Tsionas, Efthymios G. & Malikov, Emir & Kumbhakar, Subal C., 2018. "An internally consistent approach to the estimation of market power and cost efficiency with an application to U.S. banking," European Journal of Operational Research, Elsevier, vol. 270(2), pages 747-760.
    6. Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
    7. Delis, Manthos D. & Iosifidi, Maria & Tsionas, Mike, 2020. "Management estimation in banking," European Journal of Operational Research, Elsevier, vol. 284(1), pages 355-372.
    8. Tsionas, Mike G. & Izzeldin, Marwan, 2018. "A novel model of costly technical efficiency," European Journal of Operational Research, Elsevier, vol. 268(2), pages 653-664.
    9. Benjamin Hampf, 2018. "Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?," Empirical Economics, Springer, vol. 54(1), pages 101-127, February.
    10. Emir Malikov & Raushan Bokusheva & Subal C. Kumbhakar, 2018. "A hedonic-output-index-based approach to modeling polluting technologies," Empirical Economics, Springer, vol. 54(1), pages 287-308, February.
    11. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2019. "Estimating Stochastic Ray Production Frontiers," IFRO Working Paper 2019/06, University of Copenhagen, Department of Food and Resource Economics.
    12. Yaryna Kolomiytseva, 2018. "Revisiting Transformation and Directional Technology Distance Functions," Papers 1812.10108, arXiv.org.
    13. Tsionas, Mike G. & Andrikopoulos, Athanasios, 2020. "On a High-Dimensional Model Representation method based on Copulas," European Journal of Operational Research, Elsevier, vol. 284(3), pages 967-979.
    14. Mike G. Tsionas, 2020. "Remarks on Bank Competition and Convergence Dynamics," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(5), pages 1-5, May.
    15. Tsionas, Mike G. & Izzeldin, Marwan, 2018. "Smooth approximations to monotone concave functions in production analysis: An alternative to nonparametric concave least squares," European Journal of Operational Research, Elsevier, vol. 271(3), pages 797-807.
    16. Tsionas, Mike G., 2020. "A coherent approach to Bayesian Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 281(2), pages 439-448.

    More about this item

    Keywords

    Bad Outputs; Commercial Banks; Directional Distance Function; Endogeneity; MCMC; Nonperforming Loans; Productivity; Technical Change;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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