Interaction and marginal effects in nonlinear models : case of ordered logit and probit models
MetadataShow full item record
Interaction and marginal effects are often an important concern, especially when variables are allowed to interact in a nonlinear model. In a linear model, the interaction term, representing the interaction effect, is the impact of a variable on the marginal effect of another variable. In a nonlinear model, however, the marginal effect of the interaction term is different from the interaction effect. This report provides a general derivation of both effects in a nonlinear model and a linear model to clearly illustrate the difference. These differences are then demonstrated with empirical data. The empirical study shows that the corrected interaction effect in an ordered logit or probit model is substantially different from the incorrect interaction effect produced by the margins command in Stata. Based on the correct formulas, this report verifies that the interaction effect is not the same as the marginal effect of the interaction term. Moreover, we must be careful when interpreting the nonlinear models with interaction terms in Stata or any other statistical software package.
Showing items related by title, author, creator and subject.
A joint multiple discrete continuous extreme value (MDCEV) model and multinomial logit model (MNL) for examining vehicle type/vintage, make/model and usage decisions of the household Sen, Sudeshna (2006)In this dissertation, we seek to contribute to the area of automobile demand modeling by developing a comprehensive econometric model to examine several dimensions of household vehicle holdings and usage decisions. In ...
Modeling residential self-selection in activity-travel behavior models : integrated models of multidimensional choice processes Pinjari, Abdul Rawoof (2008-08)The focus of transportation planning, until the past three decades or so, was to provide adequate transportation infrastructure supply to meet the mobility needs of the population. Over the past three decades, however, in ...
Multivariate multiple-membership random effects models : a demonstration and assessment of model estimation and fit Park, Sunyoung, Ph. D. in educational psychology (2018-04-27)The current dissertation, composed of two studies, focused on the models that handle several data structure complexities simultaneously. The first study introduced and evaluated Markov Chain Monte Carlo (MCMC) estimation ...