Testing hypotheses about interaction terms in nonlinear models

Testing hypotheses about interaction terms in nonlinear models

Testing Hypotheses About Interaction Terms in Nonlinear Models

A recent, widely discussed contribution to econometric practice by Ai and Norton (2003) has proposed an approach to analyzing interaction effects of variables in nonlinear models. The authors focus attention on a binary choice (logit) model, though their results are easily extended to other nonlinear models. The main result of the study applies to a model that contains an interaction term, such as

Interaction terms in nonlinear models.

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...

Experimental models for testing hypotheses about cumulative cultural evolution

The rapid appearance (over evolutionary time) of the cognitive skills and complex inventions of modern humans has been attributed to “cumulative cultural evolution” (henceforth CCE), the accumulation of knowledge and skills over generations. To date, researchers have only been able to speculate about the reasons for the apparent absence of this phenomenon in nonhumans, and it has not been possi...

Incomplete Draft Do Not Cite Interaction Terms in Nonlinear Models

Although applied economists often estimate interaction terms to infer how the effect of one independent variable on the dependent variable depends on the magnitude of another independent variable, most researchers misinterpret the coefficient in nonlinear models. The magnitude of the interaction effect does not equal the coefficient on the interaction term, can be of opposite sign, and is cond...