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Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market

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  • Yuliya Lovcha

    (Universitat Rovira-i-Virgili and CREIP)

  • Alejandro Perez-Laborda

    (Universitat Rovira-i-Virgili and CREIP)

Abstract

A structural multivariate long memory model of the US gasoline market is employed to disentangle structural shocks and to estimate the own-price elasticity of gasoline demand. Our main empirical findings are: (1) there is strong evidence of nonstationarity and mean reversion in the real price of gasoline and in gasoline consumption; (2) accounting for the degree of persistence present in the data is essential to assess the responses of these two variables to structural shocks; (3) the contributions of the different supply and demand shocks to fluctuations in the gasoline market vary across frequency ranges; and (4) long memory makes available an interesting range of convergent possibilities for gasoline demand elasticities. Our estimates suggest that after a change in prices, consumers undertake a few measures to reduce consumption in the short- and medium-run but are reluctant to implement major changes in their consumption habits.

Suggested Citation

  • Yuliya Lovcha & Alejandro Perez-Laborda, 2017. "Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market," Empirical Economics, Springer, vol. 53(2), pages 405-422, September.
    • Handle: RePEc:spr:empeco:v:53:y:2017:i:2:d:10.1007_s00181-016-1145-x
    DOI: 10.1007/s00181-016-1145-x
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    More about this item

    Keywords

    Fractional integration; Persistence; Structural VAR; Variance-frequency decomposition;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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