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Interaction Effects and Difference-in-Difference Estimation in Loglinear Models

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  • John Mullahy

Abstract

In applied econometric work, analysts are concerned often with estimation of and inferences about interaction effects, e.g. 'Does the magnitude of the effect of z1 on y depend on z2? ' This paper develops tests for and proper interpretation of various forms of interaction effects in one prominent class of regression models loglinear models for which the nature of estimated interaction effects has not always been given due attention. The results obtained here have a direct bearing on the interpretation of so-called difference-in-difference estimates when these are obtained using loglinear models. An empirical example of the impacts of health insurance and chronic illness on prescription drug utilization underscores the importance of these issues in practical settings.

Suggested Citation

  • John Mullahy, 1999. "Interaction Effects and Difference-in-Difference Estimation in Loglinear Models," NBER Technical Working Papers 0245, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0245
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    3. Barkowski, Scott, 2021. "Interpretation of nonlinear difference-in-differences: the role of the parallel trends assumption," MPRA Paper 108975, University Library of Munich, Germany.
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    8. Hagedoorn, John & Wang, Ning, 2010. "Is there complementarity or substitutability between internal and external R&D strategies?," MERIT Working Papers 2010-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
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    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • I1 - Health, Education, and Welfare - - Health

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