Computational Statistics & Data Analysis
Rotation-based model trees for classification
International Journal of Data Analysis Techniques and Strategies
Regression analysis using the imprecise Bayesian normal model
International Journal of Data Analysis Techniques and Strategies
A comparative modelling analysis of firm performance
International Journal of Data Analysis Techniques and Strategies
A supply chain forecast and planning application for small businesses
International Journal of Data Analysis Techniques and Strategies
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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.