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Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey*

* This paper has been replicated

Author

Listed:
  • Galip Altinay

    (Bandirma Onyedi Eylul University)

  • A. Talha Yalta

    (TOBB University of Economics and Technology)

Abstract

Much of the existing literature on demand for natural gas assumes constant and single-value elasticities, overlooking the possibility of dynamic responses to the changing conditions. We aim to fill this gap by providing individual time series of short-run elasticity estimates based on maximum entropy resampling in a fixed-width rolling window framework. This approach does not only enable taking the variability of the elasticities into account, but also helps obtain more efficient and robust results in small samples in comparison with conventional inferences based on asymptotic distribution theory. To illustrate the methodology, we employ monthly time-series data between 2004 and 2012 and analyze the dynamics of residential natural gas demand in Istanbul, the largest metropolitan area in Turkey. Our findings reveal that the elasticities of the demand model do not remain constant and they are sensitive to the economic situation as well as weather fluctuations.

Suggested Citation

  • Galip Altinay & A. Talha Yalta, 2016. "Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey," Empirical Economics, Springer, vol. 51(1), pages 201-220, August.
    • Handle: RePEc:spr:empeco:v:51:y:2016:i:1:d:10.1007_s00181-015-1012-1
    DOI: 10.1007/s00181-015-1012-1
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    References listed on IDEAS

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    Cited by:

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    6. Halim Tatli, 2018. "Multiple Determinants of Household Natural Gas Demand: A Panel Data Analysis in OECD Countries," Asian Development Policy Review, Asian Economic and Social Society, vol. 6(4), pages 243-253, December.
    7. Jumah Ahmad Alzyadat, 2022. "The Price and Income Elasticity of Demand for Natural Gas Consumption in Saudi Arabia," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 357-363, November.

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    Replication

    This item has been replicated by:
  • Alptekin, Aynur & Broadstock, David C. & Chen, Xiaoqi & Wang, Dong, 2019. "Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions," Energy Economics, Elsevier, vol. 82(C), pages 26-41.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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