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Local Maximum Likelihood Techniques with Categorical Data

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Abstract

In the wake of the recent global financial crisis central banks and regulators are concerned about redirection of bailout funds into dividends. Yet, we do not know much about the extent banks follow dividend policies and funding decisions optimal to generating shareholders? wealth because banks have been mostly absent from an otherwise expansive literature on dividend policy. A relative, multi-period analysis of the troubled Japanese regional banks for the period 1998-2007 identifies inefficiencies in the levels of dividends, retained earnings, external funding and share performance. The study unfolds further by investigating associations between inefficiencies and non-performing loans, followed by a comparison of efficient versus inefficient banks across good and bad economic times. The methodology captures linkages among yearly financial decisions over multiple periods, thus summarizing long-term performance. The new approach can guide continuous benchmarking of bank financial performance, as well as help policy-makers monitoring potential misappropriation of bailout funds during financial crises. The findings indicate a potential to adjust levels of debt and equity funding, and substantial room for improvement in share performance. Associations between non-performing loans and technical inefficiencies are generally statistically significant.

Suggested Citation

  • Byeong U. Park & Leopold Simar & Valentin Zelenyuk, 2010. "Local Maximum Likelihood Techniques with Categorical Data," CEPA Working Papers Series WP142010, School of Economics, University of Queensland, Australia.
  • Handle: RePEc:qld:uqcepa:60
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    File URL: https://economics.uq.edu.au/files/5220/WP142010.pdf
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    Cited by:

    1. Valentin Zelenyuk & Leopold Simar, 2011. "To Smooth or Not to Smooth? The Case of Discrete Variables in Nonparametric Regressions," CEPA Working Papers Series WP102011, School of Economics, University of Queensland, Australia.
    2. Marijn Verschelde & Michel Dumont & Glenn Rayp & Bruno Merlevede, 2016. "Semiparametric stochastic metafrontier efficiency of European manufacturing firms," Journal of Productivity Analysis, Springer, vol. 45(1), pages 53-69, February.
    3. Li, Degui & Simar, Léopold & Zelenyuk, Valentin, 2016. "Generalized nonparametric smoothing with mixed discrete and continuous data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 424-444.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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