Discrete choice models are used to investigate the individual's choice among a discrete number of alternatives. The characteristics of each alternative, by means of multinomial and nested multinomial models, have been taken into account. Specifically, this study analyses the impact of choice-specific characteristics (economic and socio-political attributes) in a model of choice between differen...

Discrete choice models, like the multinomial logit (MNL), have long been recognized for their ability to capture a wide array of transport-related choice phenomena. However, a number of choices are continuous response variables (e.g., location choice, departure time choice, activity duration, and vehicle usage). In this paper, the continuous cross-nested logit (CCNL) model is introduced. The CC...

We study firms that sell multiple differentiated substitutable products and customers whose purchase behavior follows a Nested Logit model, of which the Multinomial Logit model is a special case. Customers make purchasing decision sequentially under the Nested Logit model: they first select a nest of products and subsequently purchase a product within the selected nest. We consider the general ...

Multinomial logit choice models based on latent class (LC) or HB methods are utilized in marketing research today along with simulators which predict choices and market shares. However, a weakness in these models creates a potential interpretability and validity problem. The problem is that the part-worth preference (utility) parameters that are used to make such predictions are generally confo...

When estimating the value of natural resources, the applied analyst must often work with noisy or otherwise imprecise measures of both dependent and independent variables. To help control for this uncertainty, this article introduces a multinomial logit model (MNL) that uses ancillary information to control for uncertainty in observed choices and in the attributes of a respondent’s choice set. ...

Multinomial logistic regression is a powerful tool to model choice from a finite set of alternatives, but it comes with an underlying model assumption called the independence of irrelevant alternatives, stating that any item added to the set of choices will decrease all other items’ likelihood by an equal fraction. We perform statistical tests of this assumption across a variety of datasets and...

Abstract This study applied a logistic regression model to determine the odds ratio of selecting clean versus unclean energy as main household fuel choice. also undertook establish coefficients factors determining A large microeconomic dataset from KIPPRA’s comprehensive and analysis on use patterns in Kenya (2010) was employed carry out analysis. multinomial logit choices cooking fuels urban K...

Through a Conservation Effects Assessment Project (CEAP) partnership, University of Missouri investigators used GPS satellite transmitters to track and assess wetland habitat use by midcontinent mallards, including the contribution of Wetlands Reserve Program (WRP) easements to habitats selected during the non-breeding season. Investigators used multinomial discrete choice models that accounted...

Discrete choice models are commonly used by applied statisticians in numerous fields, such as marketing, economics, finance, and operations research. When agents in discrete choice models are assumed to have differing preferences, exact inference is often intractable. Markov chain Monte Carlo techniques make approximate inference possible, but the computational cost is prohibitive on the large ...