On interaction effects

dc.contributor.authorFrondel, Manuel
dc.contributor.authorVance, Colin
dc.date.accessioned2012-01-18T13:25:18Z
dc.date.available2012-01-18T13:25:18Z
dc.date.issued2012-01-18
dc.description.abstractInteraction effects capture the impact of one explanatory variable x1 on the marginal effect of another explanatory variable x2. To explore interaction effects, socalled interaction terms x1x2 are typically included in estimation specifications. While in linear models the effect of a marginal change in the interaction term is equal to the interaction effect, this equality generally does not hold in non-linear specifications (AI,NORTON, 2003). This paper provides for a general derivation of interaction effects in both linear and non-linear models and calculates the formulae of the interaction effects resulting from HECKMAN’s sample selection model as well as the Two-Part Model, two regression models commonly applied to data with a large fraction of either missing or zero values in the dependent variable, respectively. Drawing on a survey of automobile use from Germany, we argue that while it is important to test for the significance of interaction effects, their size conveys limited substantive content. More meaningful, and also more easy to grasp, are the conditional marginal effects pertaining to two variables that are assumed to interact.en
dc.identifier.urihttp://hdl.handle.net/2003/29285
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-3261
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;01/2012en
dc.subjectinteraction termsen
dc.subjecttruncated regression modelsen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleOn interaction effectsen
dc.title.alternativeThe case of heckit and two-part modelsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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