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Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects

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  • Pavlos Almanidis

Abstract

This paper investigates the existence of heterogeneous technologies in the US commercial banking industry through the nondynamic panel threshold effects estimation technique proposed by Hansen (Econometrica 64:413–430, 1999 , Econometrica 68:575–603, 2000a ). We employ the total assets as a threshold variable, which is typically considered as a proxy for bank’s size in the banking literature. We modify the threshold effects model to allow for time-varying effects, wherein these are modeled by a time polynomial of degree two as in Cornwell et al. (J Econom 46:185–200, 1990 ) model. Threshold effects estimation allows us to sort banks into discrete groups based on their size in a structural and consistent manner. We determine seven such distinct technology-groups within which banks are allowed to share the same technology parameters. We provide estimates of individual and group efficiency scores, as well as of those of returns to scale and measures of technological change for each group. The presence of the threshold(s) is tested via bootstrap procedure outlined in Hansen (Econometrica 64:413–430, 1999 ) and the relationship between bank size and efficiency ratios is investigated. Copyright Springer Science+Business Media, LLC 2013

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  • Pavlos Almanidis, 2013. "Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects," Journal of Productivity Analysis, Springer, vol. 39(2), pages 191-205, April.
  • Handle: RePEc:kap:jproda:v:39:y:2013:i:2:p:191-205
    DOI: 10.1007/s11123-012-0306-y
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    6. Kutlu, Levent & McCarthy, Patrick, 2016. "US airport ownership, efficiency, and heterogeneity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 117-132.
    7. Zhang, Jingfang & Malikov, Emir, 2022. "Off-balance sheet activities and scope economies in U.S. banking," Journal of Banking & Finance, Elsevier, vol. 141(C).
    8. Pavlos Almanidis & Giannis Karagiannis & Robin Sickles, 2015. "Semi-nonparametric spline modifications to the Cornwell–Schmidt–Sickles estimator: an analysis of US banking productivity," Empirical Economics, Springer, vol. 48(1), pages 169-191, February.
    9. Mohamed Chaffai & Patrick Plane, 2017. "Firm Productivity, Technology and Export Status, What Can We Learn from Egyptian Industries?," Working Papers 1134, Economic Research Forum, revised 09 Jun 2017.
    10. Elizabeth Ahikiriza & Jef Meensel & Xavier Gellynck & Ludwig Lauwers, 2021. "Heterogeneity in frontier analysis: does it matter for benchmarking farms?," Journal of Productivity Analysis, Springer, vol. 56(2), pages 69-84, December.
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    More about this item

    Keywords

    Stochastic frontier; Threshold effects; Panel data; Banks; C13; C23; D24; G21;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single 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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