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Estimating Short and Long-Run Demand Elasticities: A Primer with Energy-Sector Applications

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  • Cuddington, John
  • Dagher, Leila

Abstract

Many empirical exercises estimating demand functions, whether in energy economics or other fields, are concerned with estimating dynamic effects of price and income changes over time. This paper first reviews a number of commonly used dynamic demand specifications to highlight the implausible a priori restrictions that they place on short and long-run elasticities. Such problems are easily avoided by adopting a general-to-specific modeling methodology. Second, it discusses functional forms and estimation issues for getting point estimates and associated standard errors for both short and long-run elasticities – key information that is missing from many published studies. Third, our proposed approach is illustrated using a dataset on Minnesota residential electricity demand.

Suggested Citation

  • Cuddington, John & Dagher, Leila, 2013. "Estimating Short and Long-Run Demand Elasticities: A Primer with Energy-Sector Applications," MPRA Paper 116122, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:116122
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    References listed on IDEAS

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    1. Stock, James H & Watson, Mark W, 1988. "Variable Trends in Economic Time Series," Journal of Economic Perspectives, American Economic Association, vol. 2(3), pages 147-174, Summer.
    2. Alberini, Anna & Gans, Will & Velez-Lopez, Daniel, 2011. "Residential consumption of gas and electricity in the U.S.: The role of prices and income," Energy Economics, Elsevier, vol. 33(5), pages 870-881, September.
    3. Dagher, Leila, 2012. "Natural gas demand at the utility level: An application of dynamic elasticities," Energy Economics, Elsevier, vol. 34(4), pages 961-969.
    4. Dahl, Carol A., 1993. "A survey of energy demand elasticities in support of the development of the NEMS," MPRA Paper 13962, University Library of Munich, Germany.
    5. Huntington, Hillard G., 2007. "Industrial natural gas consumption in the United States: An empirical model for evaluating future trends," Energy Economics, Elsevier, vol. 29(4), pages 743-759, July.
    6. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    7. Alberini, Anna & Gans, Will & Velez-Lopez, Daniel, 2011. "Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income," Sustainable Development Papers 99637, Fondazione Eni Enrico Mattei (FEEM).
    8. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, April.
    9. Franklin M. Fisher & Paul H. Cootner & Martin N. Baily, 1972. "An Econometric Model of the World Copper Industry," Bell Journal of Economics, The RAND Corporation, vol. 3(2), pages 568-609, Autumn.
    10. Arsenault, E. & Bernard, J. -T. & Carr, C. W. & Genest-Laplante, E., 1995. "A total energy demand model of Quebec : Forecasting properties," Energy Economics, Elsevier, vol. 17(2), pages 163-171, April.
    11. David F. Hendry & Katarina Juselius, 2001. "Explaining Cointegration Analysis: Part II," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 75-120.
    12. Mizon, Grayham E., 1995. "A simple message for autocorrelation correctors: Don't," Journal of Econometrics, Elsevier, vol. 69(1), pages 267-288, September.
    13. West, Kenneth D, 1988. "Asymptotic Normality, When Regressors Have a Unit Root," Econometrica, Econometric Society, vol. 56(6), pages 1397-1417, November.
    14. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
    15. Wojciech W. Charemza & Derek F. Deadman, 1997. "New Directions In Econometric Practice, Second Edition," Books, Edward Elgar Publishing, number 1139.
    16. Urbain, Jean-Pierre, 1992. "On Weak Exogeneity in Error Correction Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(2), pages 187-207, May.
    17. Bernard, Jean-Thomas & Bolduc, Denis & Yameogo, Nadège-Désirée, 2011. "A pseudo-panel data model of household electricity demand," Resource and Energy Economics, Elsevier, vol. 33(1), pages 315-325, January.
    18. Garcia-Cerrutti, L. Miguel, 2000. "Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms," Resource and Energy Economics, Elsevier, vol. 22(4), pages 355-366, October.
    19. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    20. Chern, Wen S. & Just, Richard E., 1980. "Regional analysis of electricity demand growth," Energy, Elsevier, vol. 5(1), pages 35-46.
    21. Pei, Fanyu & Tilton, John E., 1999. "Consumer preferences, technological change, and the short-run income elasticity of metal demand," Resources Policy, Elsevier, vol. 25(2), pages 87-109, June.
    22. Stock, James H, 1987. "Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors," Econometrica, Econometric Society, vol. 55(5), pages 1035-1056, September.
    23. Lin Chan, Hing & Kam Lee, Shu, 1997. "Modelling and forecasting the demand for coal in China," Energy Economics, Elsevier, vol. 19(3), pages 271-287, July.
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    More about this item

    Keywords

    SR elasticity; LR elasticity; demand function; ADL;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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