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Threshold effects of financial status on the cost frontiers of financial institutions in nondynamic panels

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  • Mei-Hui Wang
  • Tai-Hsin Huang

Abstract

This article applies Hansen's (1999, 2000) threshold regression model to estimate translog cost frontiers in the hope of shedding light on the banking industry's production processes and the extent of its Technical Efficiency (TE). The threshold technique allows for the existence of multiple technologies of production, distinguished by an exogenous threshold variable. Strong evidence of multiple technologies is found in the industry irrespective of which financial indicator, as constructed by factor analysis, defines the threshold variable. Cost savings and scale economies among the various underlying technologies are compared herein. We also highlight the differences between the threshold results and the conventional cost frontier.

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  • Mei-Hui Wang & Tai-Hsin Huang, 2009. "Threshold effects of financial status on the cost frontiers of financial institutions in nondynamic panels," Applied Economics, Taylor & Francis Journals, vol. 41(26), pages 3389-3401.
    • Handle: RePEc:taf:applec:v:41:y:2009:i:26:p:3389-3401
    DOI: 10.1080/00036840802600079
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    Cited by:

    1. Hung-pin Lai, 2013. "Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable," Journal of Productivity Analysis, Springer, vol. 40(2), pages 227-237, October.
    2. Badunenko, Oleg & D’Inverno, Giovanna & De Witte, Kristof, 2023. "On distinguishing the direct causal effect of an intervention from its efficiency-enhancing effects," European Journal of Operational Research, Elsevier, vol. 310(1), pages 432-447.
    3. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2012. "Is Globalization Driving Efficiency? A Threshold Stochastic Frontier Panel Data Modeling Approach," Review of International Economics, Wiley Blackwell, vol. 20(3), pages 563-579, August.

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