On the Economic Meaning of Interaction Term Coefficients in Non-Linear Binary Response Regression Models

By Adam Kolasinski, Andrew F. Siegel. 2010

81 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3afedb4b2aab858bda75ed0f745debfc325bc9b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. G. Karolyi
2. René M. Stulz
3. Craig Doidge
4. Kristin J. Forbes
5. Dirk G. Baur
6. Albert K. Tsui
7. Christian Upper
8. Y. Tse

In Statistics and Probability

1. Craig Doidge
2. G. Karolyi
3. René M. Stulz

Citations in the corpus

• The Effect of Liquidity on Governance (2011, unknown): “, Kole (1997)).”
• Short Selling and Earnings Management: A Controlled Experiment (2016, unknown): “Le (1998) and Kolasinski and Siegel (2010), however, argue that the coefficient on the interaction term is relevant even in a nonlinear regression, especially when used to measure proportional rather than absolute marginal effects.”
• The U.S. left behind? Financial globalization and the rise of IPOs outside the U.S. (2013, background): “We, therefore, report the regression coefficients, but interpret them in terms of odds ratios which are simpler to interpret when there are interaction terms in the model (see, e.g., Buis, 2010; Kolasinski and Siegel, 2010).”