IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/116122.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/116122/1/MPRA_paper_116122.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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, December.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots," NBER Chapters, in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220, National Bureau of Economic Research, Inc.
    2. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    3. Myers, Robert J., 1994. "Time Series Econometrics and Commodity Price Analysis: A Review," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 62(02), pages 1-15, August.
    4. Nnaemeka Vincent Emodi & Taha Chaiechi & ABM Rabiul Alam Beg, 2018. "The impact of climate change on electricity demand in Australia," Energy & Environment, , vol. 29(7), pages 1263-1297, November.
    5. Shahriyar Aliyev & Evžen Kočenda, 2023. "ECB monetary policy and commodity prices," Review of International Economics, Wiley Blackwell, vol. 31(1), pages 274-304, February.
    6. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).
    7. Csereklyei, Zsuzsanna, 2020. "Price and income elasticities of residential and industrial electricity demand in the European Union," Energy Policy, Elsevier, vol. 137(C).
    8. Kulshreshtha, Mudit & Parikh, Jyoti K., 2000. "Modeling demand for coal in India: vector autoregressive models with cointegrated variables," Energy, Elsevier, vol. 25(2), pages 149-168.
    9. Kostakis, Ioannis & Lolos, Sarantis & Sardianou, Eleni, 2021. "Residential natural gas demand: Assessing the evidence from Greece using pseudo-panels, 2012–2019," Energy Economics, Elsevier, vol. 99(C).
    10. Shabtai Donnenfeld & Alfred A. Haug, 2008. "Currency invoicing of US imports," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 13(2), pages 184-198.
    11. Miller, Mark & Alberini, Anna, 2016. "Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data," Energy Policy, Elsevier, vol. 97(C), pages 235-249.
    12. Bardsen, Gunnar, 1990. "Dynamic Modelling And The Demand For Narrow Money In Norway," Economic Research Papers 268479, University of Warwick - Department of Economics.
    13. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    14. Mark Miller & Anna Alberini, 2015. "Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data," CER-ETH Economics working paper series 15/223, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    15. Li, Lanlan & Gong, Chengzhu & Tian, Shizhong & Jiao, Jianling, 2016. "The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation," Energy, Elsevier, vol. 96(C), pages 48-58.
    16. James H. Stock & Mark W. Watson, 1989. "A Simple MLE of Cointegrating Vectors in Higher Order Integrated Systems," NBER Technical Working Papers 0083, National Bureau of Economic Research, Inc.
    17. Dorothee Charlier and Sondes Kahouli, 2019. "From Residential Energy Demand to Fuel Poverty: Income-induced Non-linearities in the Reactions of Households to Energy Price Fluctuations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    18. Burke, Paul J. & Yang, Hewen, 2016. "The price and income elasticities of natural gas demand: International evidence," Energy Economics, Elsevier, vol. 59(C), pages 466-474.
    19. Gautam, Tej K. & Paudel, Krishna P., 2018. "The demand for natural gas in the Northeastern United States," Energy, Elsevier, vol. 158(C), pages 890-898.
    20. Ying Qian & Duncan, Ronald & DEC, 1994. "Optimal hedging strategy revisited : acknowledging the existence of nonstationary economic timeseries," Policy Research Working Paper Series 1279, The World Bank.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:116122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.