A Reinterpretation of Interactions in Regressions
AbstractRegression specifications in applied econometrics frequently employ regressors that are defined as the product of two other regressors to form an interaction. Unfortunately, the interpretation of the results of these models is not as straight forward as in the linear case. In this paper, we present a method for drawing inferences for interaction models by defining the partial influence function. We present an example that demonstrates how one may draw new inferences by constructing the confidence intervals for the partial influence functions based on the traditional published findings for regressions with interaction terms.
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Bibliographic InfoPaper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 1015.
Length: 13 pages
Date of creation: 2007
Date of revision:
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More information through EDIRC
Interaction effects; dummy variables; linear transformation; Fieller method;
Other versions of this item:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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- Woessmann, Ludger & West, Martin R., 2002.
"Class-Size Effects in School Systems Around the World: Evidence from Between-Grade Variation in TIMSS,"
IZA Discussion Papers
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- Wo[ss]mann, Ludger & West, Martin, 2006. "Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS," European Economic Review, Elsevier, vol. 50(3), pages 695-736, April.
- Ludger Wößmann & Martin R. West, 2002. "Class-Size Effects in School Systems Around the World: Evidence from Between-Grade Variation in TIMSS," Kiel Working Papers 1099, Kiel Institute for the World Economy.
- Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule To Estimate The Effect Of Class Size On Scholastic Achievement," The Quarterly Journal of Economics, MIT Press, vol. 114(2), pages 533-575, May.
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