Interpreting Dummy Variables in Semi-logarithmic Regression Models: Exact Distributional Results
AbstractCare must be taken when interpreting the coefficients of dummy variables in semi-logarithmic regression models. Existing results in the literature provide the best unbiased estimator of the percentage change in the dependent variable, implied by the coefficient of a dummy variable, and of the variance of this estimator. We extend these results by establishing the exact sampling distribution of an unbiased estimator of the implied percentage change. This distribution is nonnormal, and is positively skewed in small samples. We discuss the construction of bootstrap confidence intervals for the implied percentage change, and illustrate our various results with two applications: one involving a wage equation, and one involving the constructions of an hedonic price index for computer disk drives.
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Bibliographic InfoPaper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 1101.
Length: 24 pages
Date of creation: 31 Jan 2011
Date of revision:
Note: ISSN 1485-6441
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Semi-logarithmic regression; dummy variable; percentage change; confidence interval;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- Kees Jan Van Garderen & Chandra Shah, 2002. "Exact interpretation of dummy variables in semilogarithmic equations," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 149-159, June.
- Mick Silver, 2012. "Why House Price Indexes Differ: Measurement and Analysis," IMF Working Papers 12/125, International Monetary Fund.
- Nicolas G. Vaillant & François-Charles Wolff, 2013. "Understanding diamond pricing using unconditional quantile regressions," Working Papers halshs-00853384, HAL.
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