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A Bayesian sampling approach to measuring the price responsiveness of gasoline demand using a constrained partially linear model

Author

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  • Chen, Haotian
  • Smyth, Russell
  • Zhang, Xibin
Abstract
Partial linear models provide an intuitively appealing way to examine gasoline demand because one can examine how response to price varies according to the price level and people's income. However, despite their intuitive appeal, partial linear models have tended to produce implausible and/or erratic price effects. Blundell et al. (2012) propose a solution to this problem that involves using Slutsky shape restrictions to improve the precision of the nonparametric estimate of the demand function. They propose estimating a constrained partially linear model through three steps, where the weights are optimized by minimizing an objective function under the Slutsky constraint, bandwidths are selected through least squares cross-validation, and linear coefficients are estimated using profile least squares. A limitation of their three-step estimation method is that bandwidths are selected based on pre-estimated parameters. We improve on the Blundell et al. (2012) solution in that we derive a posterior and develop a posterior simulation algorithm to simultaneously estimate the linear coefficients, bandwidths in the kernel estimator and the weights imposed by the Slutsky condition. With our proposed sampling algorithm, we estimate a constrained partially linear model of household gasoline demand employing household survey data for the United States for 1991 and 2001 and for Canada for 2006–2009 and find plausible price effects.

Suggested Citation

  • Chen, Haotian & Smyth, Russell & Zhang, Xibin, 2017. "A Bayesian sampling approach to measuring the price responsiveness of gasoline demand using a constrained partially linear model," Energy Economics, Elsevier, vol. 67(C), pages 346-354.
    • Handle: RePEc:eee:eneeco:v:67:y:2017:i:c:p:346-354
    DOI: 10.1016/j.eneco.2017.08.029
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    References listed on IDEAS

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    More about this item

    Keywords

    Kernel estimator; Markov chain Monte Carlo; Price elasticity; Slutsky condition; Smoothness;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

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