They would like to thank participants at the American Accounting Association Annual meeting, San Francisco, 2005; participants at the University of Sydney research seminar series and Bill Greene, Ken Train and David Johnstone. Comments from an anonymous referee are also much appreciated.
Evaluating the Behavioural Performance of Alternative Logit Models: An Application to Corporate Takeovers Research
Article first published online: 9 OCT 2007
Journal of Business Finance & Accounting
Volume 34, Issue 7-8, pages 1193–1220, September/October 2007
How to Cite
Jones, S. and Hensher, . D. A. (2007), Evaluating the Behavioural Performance of Alternative Logit Models: An Application to Corporate Takeovers Research. Journal of Business Finance & Accounting, 34: 1193–1220. doi: 10.1111/j.1468-5957.2007.02049.x
- Issue published online: 9 OCT 2007
- Article first published online: 9 OCT 2007
- (Paper received March 2006, revised version accepted March 2007. Online publication August 2007)
- nested logit;
- mixed logit;
- latent class models;
- corporate takeovers
Abstract: Econometric models involving a discrete outcome dependent variable abound in the finance and accounting literatures. However, much of the literature to date utilises a basic or standard logit model. Capitalising on recent developments in the discrete choice literature, we examine three advanced (or non-IID) logit models, namely: nested logit, mixed logit and latent class MNL. Using an illustration from corporate takeovers research, we compare the explanatory and predictive performance of each class of advanced model relative to the standard model. We find that in all cases the more advanced logit model structures, which correct for the highly restrictive IID and IIA conditions, provide significantly greater explanatory power than standard logit. Mixed logit and latent class MNL models exhibited the highest overall predictive accuracy on a holdout sample, while the standard logit model performed the worst. Moreover, the analysis of marginal effects of all models indicates that use of advanced models can lead to more insightful and behaviourally meaningful interpretations of the role and influence of explanatory variables and parameter estimates in model estimation. The results of this paper have implications for the use of more optimal logit structures in future research and practice.