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A composite logistic regression approach for ordinal panel data regression

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  • Ronghua Luo
  • Hansheng Wang

Abstract

We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001; Hsiao, 2003) can be directly applied. As a result, the new method allows the unobserved subject effects to be correlated with the observed predictors in an arbitrary manner. Computationally, the new method is able to profile out unobserved subject effects in a very neat manner. This not only makes computational implementation very easy but also makes theoretical treatment straightforward. In particular, we show theoretically that the resulting estimator is √n-consistent and asymptotically normal. Both simulations and a real example are reported to demonstrate the usefulness of the new method.

Suggested Citation

  • Ronghua Luo & Hansheng Wang, 2008. "A composite logistic regression approach for ordinal panel data regression," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 29-43.
    • Handle: RePEc:ids:injdan:v:1:y:2008:i:1:p:29-43
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    Cited by:

    1. Owen P. Hall Jr. & Darrol J. Stanley, 2012. "A comparative modelling analysis of firm performance," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 43-56.
    2. Meena Badade & T. V. Ramanathan, 2020. "Probabilistic frontier regression model for multinomial ordinal type output data," Journal of Productivity Analysis, Springer, vol. 53(3), pages 339-354, June.

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