Logit Kernel is a discrete choice model that has both probit-like disturbances as well as an additive i.i.d. extreme value (or Gumbel) disturbance à la multinomial logit. The result is an intuitive, practical, and powerful model that combines the flexibility of probit (and more) with the tractability of logit. For this reason, logit kernel has been deemed the “model of the future” and is becomi...

Many researchers have used the conditional logit model to examine migration. One common objection to this model is that it carries the independence from irrelevant alternatives (IIA) assumption, which may be too restrictive. This study compares the conditional logit with models that partially relax (nested logit) or fully relax (mixed logit) the IIA assumption. We will begin to learn whether as...

Empirical studies on household car ownership have used two types of discrete choice modelling structures, the ordered and the unordered. In ordered structures such as the ordered logit and ordered probit models, the choice of the number of household-vehicles arises from a uni-dimensional latent index that reflects the propensity of a household to own vehicles. Unordered response models, on the ...

This article deals with a very simple issue: if we have grouped data with a binary dependent variable and want to include fixed effects (group specific intercepts) in the specification, is Ordinary Least Squares (OLS) in any way superior to a (conditional) logit form? In particular, what are the consequences of using OLS instead of a fixed effects logit model with respect to the latter dropping...

Network formation among individuals constitutes an important part of many OR processes, but relatively little is known about how individuals make their linking decisions in networks. This article provides an investigation of heuristic effects in individual linking decisions for network formation in an incentivized lab-experimental setting. Our mixed logit analysis demonstrates that the inherent...

Theory: The spatial model of elections can better be represented by using conditional logit than by multinomial logit. The spatial model, and random utility models in general, suffer from a failure to adequately consider the sub­ stitutability of candidates sharing similar or identical issue positions. Hypotheses: Multinomial logit is not much better than successive applications of binomial log...

Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted examples with higher than non-targeted ones. Recent studies reveal that traditional Cross-Entropy (CE) loss function is insufficient learn transferable due issue vanishing gradient. In this work, we provide a comprehensive ...

We estimate random-parameter logit models of anglers' choice of fishing site. The models generalize logit by allowing coefficients to vary randomly over anglers rather than being fixed. The models do not exhibit the restrictive "independence from irrelevant alternatives property" of logit and can represent any substitution pattern. Estimation explicitly accounts for the fact that the variation ...

The nested logit model has been used extensively to model multi-dimensional choice situations. A drawback of the nested logit model is that it does not allow choice alternatives to share common unobserved attributes along all the dimensions characterizing the multi-dimensional choice context. This paper formulates a mixed multinomial logit structure that accommodates unobserved correlation acro...