Further Results on Interpreting Coefficients in Regressions with a Logarithmic Dependent Variable
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Aren Megerdichian
Abstract
Estimators are presented for quantifying the proportional rate of change in the continuous variable Y from a regression in which the dependent variable is the logarithm of Y, and the data generation process includes explanatory variables of interest that may be binary (dummy), continuous, or logarithmic. Estimators from earlier works that examine the binary explanatory variable are special cases of the results presented here. The additional estimators provided here will be useful to practitioners who must convert coefficients estimated from regression models specified with a logarithmic dependent variable into proportional rates of change.
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Supplemental Material
The online version of this article (DOI: https://doi.org/10.1515/jem-2016-0015) offers supplementary material, available to authorized users.
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- Practitioner’s Corner
- Working with Data: Two Empiricists’ Experience
- Further Results on Interpreting Coefficients in Regressions with a Logarithmic Dependent Variable
- Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?
Articles in the same Issue
- Research Articles
- Exponential Regression of Fractional-Response Fixed-Effects Models with an Application to Firm Capital Structure
- Inference with Difference-in-Differences Revisited
- Local Semi-Parametric Efficiency of the Poisson Fixed Effects Estimator
- On the Validity of Edgeworth Expansions and Moment Approximations for Three Indirect Inference Estimators
- Linearly Transforming Variables in the VAR Model, How Does it Change the Impulse Response?
- Foreign Direct Investment and Growth Symbiosis: A Semiparametric System of Simultaneous Equations Analysis
- Practitioner’s Corner
- Working with Data: Two Empiricists’ Experience
- Further Results on Interpreting Coefficients in Regressions with a Logarithmic Dependent Variable
- Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?
