Interpreting interaction terms in linear and non-linear models: A cautionary tale
Interaction terms are often misinterpreted in the empirical economics literature by assuming that the coefficient of interest represents unconditional marginal changes. I present the correct way to estimate conditional marginal changes in a series of non-linear models including (ordered) logit/probit regressions, censored and truncated regressions. The linear regression model is used as the benchmark case.
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- Greene, William, 2010.
"Testing hypotheses about interaction terms in nonlinear models,"
Elsevier, vol. 107(2), pages 291-296, May.
- William H. Greene, 2009. "Testing Hypotheses About Interaction Terms in Nonlinear Models," Working Papers 09-08, New York University, Leonard N. Stern School of Business, Department of Economics.
- Brambor, Thomas & Clark, William Roberts & Golder, Matt, 2006. "Understanding Interaction Models: Improving Empirical Analyses," Political Analysis, Cambridge University Press, vol. 14(01), pages 63-82, December.
- Andreas C. Drichoutis & Rodolfo M. Nayga, 2011. "Marginal Changes in Random Parameters Ordered Response Models with Interaction Terms," Econometric Reviews, Taylor & Francis Journals, vol. 30(5), pages 565-576, October.
- Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July. Full references (including those not matched with items on IDEAS)
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