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Evaluating the Behavioural Performance of Alternative Logit Models: An Application to Corporate Takeovers Research
Author(s) -
Jones Stewart,
Hensher David A.
Publication year - 2007
Publication title -
journal of business finance and accounting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.282
H-Index - 77
eISSN - 1468-5957
pISSN - 0306-686X
DOI - 10.1111/j.1468-5957.2007.02049.x
Subject(s) - logit , mixed logit , logistic regression , econometrics , discrete choice , explanatory power , sample (material) , econometric model , latent class model , latent variable , class (philosophy) , economics , nested logit , ordered logit , statistics , computer science , mathematics , artificial intelligence , philosophy , chemistry , epistemology , chromatography
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.