A composite logistic regression approach for ordinal panel data regression

Ronghua Luo and Hansheng Wang ()

International Journal of Data Analysis Techniques and Strategies, 2008, vol. 1, issue 1, 29-43

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.

Keywords: composite logistic regression; CLR; conditional logistic regression; ordinal response; panel data; unobserved subject effect. (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.inderscience.com/link.php?id=20021 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:1:y:2008:i:1:p:29-43

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().