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Bayesian analysis of dynamic effects in inefficiency : evidence from the Colombian banking sector

Listed author(s):
  • Wiper, Michael Peter
  • Lopes Moreira Da Veiga, María Helena
  • Galán Camacho, Jorge Eduardo

Firms face a continuous process of technological and environmental changes that implies making managerial decisions in a dynamic context. However, costs and other constraints prevent firms from making instant adjustments towards optimal conditions and may cause inefficiency to be persistent in time. In this work, we propose a flexible dynamic model that makes possible to distinguish persistent effects in the inefficiency from firm inefficiency heterogeneity and to capture differences in the adjustment costs between firms. The new model is fitted to a ten year sample of Colombian banks. Our findings suggest that firm characteristics associated to size and foreign ownership have negative effects on inefficiency and separating these heterogeneity factors from the dynamics of inefficiency improves model fit. On the other hand, acquisitions are found to have positive and persistent effects on inefficiency. Colombian banks are found to present high inefficiency persistence but there exist important differences between institutions. In particular, merged banks present low costs of adjustment that allow them to recover rapidly the efficiency losses derived from merging processes

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File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/17275/ws131918.pdf?sequence=1
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Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number ws131918.

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Date of creation: Jun 2013
Handle: RePEc:cte:wsrepe:ws131918
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

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  1. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
  2. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
  3. Jalal D. Akhavein & Allen N. Berger & David B. Humphrey, 1996. "The Effects of Megamergers on Efficiency and Prices: Evidence from a Bank Profit Function," Center for Financial Institutions Working Papers 96-03, Wharton School Center for Financial Institutions, University of Pennsylvania.
  4. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676.
  5. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
  6. Yong Li & Tao Zeng & Jun Yu, 2012. "Robust Deviance Information Criterion for Latent Variable Models," Working Papers 30-2012, Singapore Management University, School of Economics.
  7. Seung Ahn & Robin Sickles, 2000. "Estimation of long-run inefficiency levels: a dynamic frontier approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 461-492.
  8. George Assaf, A. & Matousek, Roman & Tsionas, Efthymios G., 2013. "Turkish bank efficiency: Bayesian estimation with undesirable outputs," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 506-517.
  9. Grigorios Emvalomatis & Spiro E. Stefanou & Alfons Oude Lansink, 2010. "A Reduced-Form Model for Dynamic Efficiency Measurement: Application to Dairy Farms in Germany and The Netherlands," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(1), pages 161-174.
  10. Grigorios Emvalomatis, 2012. "Adjustment and unobserved heterogeneity in dynamic stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 37(1), pages 7-16, February.
  11. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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