OBJECTIVES To explain the use of interaction terms in nonlinear models. STUDY DESIGN We discuss the motivation for including interaction terms in multivariate analyses. We then explain how the straightforward interpretation of interaction terms in linear models changes in nonlinear models, using graphs and equations. We extend the basic results from logit and probit to difference-in-differenc...

Consideration set models relax the assumption that consumers are aware of all available options. Thus far, identification arguments for these models have relied either on auxiliary data on what options were considered or on instruments excluded from consideration or utility. In a discrete choice framework subsuming logit, probit and random coefficients models, we prove that utility and consider...

Although several studies have used logit or probit models and their variants to fit data of accident severity on roadway segments, few have investigated accident severity at a railroad grade crossing (RGC). Compared to accident risk analysis in terms of accident frequency and severity of a highway system, investigation of the factors contributing to traffic accidents at an RGC may be more compl...

Mixed logit is a flexible discrete choice model that allows for random coefficients and/or error components that induce correlation over alternatives and time. Procedures for estimating mixed logits have been developed within both the classical (e.g., Revelt and Train, 1998) and Bayesian traditions (Sawtooth Software, 1999.) Asymptotically, the two procedures provide the same information, and H...

Fixed effects estimators of panel models can be severely biased because of the wellknown incidental parameters problem. We develop analytical and jackknife bias corrections for nonlinear models with both individual and time effects. For asymptotics where the time-dimension (T ) grows with the cross-sectional dimension (N), the time effects introduce additional incidental parameter bias. As the ...

A linearized version of Pinkse and Slade’s (1998) spatial probit estimator is used to account for the tendency of auto supplier plants to cluster together. By reducing estimation to two steps – standard probit or logit followed by two-stage least squares – linearization produces a model that can be estimated using large datasets. Our results imply significant clustering among older plants. Supp...

Income poverty remains the main root of urban food insecurity as purchased represents a major component spending residents. The rapid and increasing urbanization sprawl are concerns for security in Mali. In Mali, 3.6% households, severely insecure prevalence rate households is 7.5%. effect on households’ was assessed literature using only single measure security. this regard, study analyzed bot...

DCM (Discrete Choice Models) is a package for estimating a class of discrete choice models. DCM is a class written in Ox, that implements a wide range of discrete choice models including standard binary response models, with notable extensions including conditional mixed logit, mixed probit, multinomial probit, and random coefficient ordered choice models. The current version can handle both cr...