Abstract
We use a semi-parametric conditional median as a robust alternative to the parametric conditional mean to estimate the gasoline demand function. Our approach protects against data and specification errors, and may yield a more reliable basis for public-policy decisions that depend on accurate estimates of gasoline demand. As a comparison, we also estimated the parametric translog conditional mean model. Our semi-parametric estimates imply that gasoline demand becomes more price elastic, but also less income elastic, as incomes rise. In addition, we find that demand appears to become more price elastic as prices increase in real terms.
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Notes
- 1.
The effective dimension of the estimated conditional median function is the number of interpolated q i . Its value varies between N and the number of explanatory variables in (3) plus one (for the intercept). We can treat k as the equivalent number of independent variables needed in a fully parametric model to reproduce the semi-parametric estimated conditional median. When k = N, there is no degree of freedom and the estimated conditional median function passes through every response observation.
- 2.
Since we have measured all variables in logarithmic form, the slope of each log-linear segment of the demand curve corresponds to the elasticity.
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Acknowledgements
We thank Steve Craig for sharing his data on population and income, which was incorporated into the data set used in this study. We are also very grateful to Rajeev Goel for sending us his gasoline and consumption data. Any remaining errors in the data are, of course, our own responsibility.
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Ng, P.T., Smith, J.L. (2015). The Elasticity of Demand for Gasoline: A Semi-parametric Analysis. In: Beran, J., Feng, Y., Hebbel, H. (eds) Empirical Economic and Financial Research. Advanced Studies in Theoretical and Applied Econometrics, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-03122-4_11
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