Interpreting logit models

Conditional Logit Models

Probit and nested logit models based on fuzzy measure

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and probit models to consider the interaction among the attributes. Besides the DM's attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make us...

Multinomial logit random effects models

This article presents a general approach for logit random effects modelling of clustered ordinal and nominal responses. We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models. Maximum likelihood estimation utilizes adaptive Gauss–Hermite quadrature within a quasi-Newton maximization algorithm. For cases in which this is computationall...

Interpreting Cointegrated Models

Error-correction models for cointegrated economic variables are commonly interpreted as reflecting partial adjustment of one variable to another. We show that error-correction models may also arise because one variable forecasts another. Reduced-form estimates of error-correction models cannot be used to distinguish these interpretations. In an application, we show that the estimated coefficien...

Interpreting News Recommendation Models

This paper presents an approach for recommending news articles on a large news portal. Focus is given to interpretability of the developed models, analysis of their performance, and deriving understanding of short and long-term user behavior on a news portal.