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Modelling and Forecasting Residential Electricity Consumption in the U.S. Mountain Region

Author

Listed:
  • Jason B. Jorgensen

    () (George Washington University)

  • Fred Joutz

    () (George Washington University)

Abstract

In this paper we present an analysis of the demand for residential electricity of the U.S. mountain region. The objective is to develop two simulations analyzing how changes in electricity prices and warmer weather affect electricity consumption and greenhouse gas emissions. Electricity demand is modeled as a function of the price of electricity, real personal income, number of households, weather as a function of heating and cooling days, and the price of natural gas. A general-to-specific approach is used to develop congruent models. We are able to estimate an equilibrium correction model capturing long run electricity demand and short run or seasonal responses. We find that in the long-run, income elasticity is positive and inelastic, own-price elasticity is negative and inelastic, and cross-price elasticity is positive and inelastic. In the short-run, all price and income elasticities are perfectly inelastic and the only effects on demand for electricity are weather variables.

Suggested Citation

  • Jason B. Jorgensen & Fred Joutz, 2012. "Modelling and Forecasting Residential Electricity Consumption in the U.S. Mountain Region," Working Papers 2012-003, The George Washington University, Department of Economics, Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2012-003
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    File URL: https://www2.gwu.edu/~forcpgm/2012-003.pdf
    File Function: First version, 2012
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    References listed on IDEAS

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    1. Franses, Philip Hans, 1991. "Seasonality, non-stationarity and the forecasting of monthly time series," International Journal of Forecasting, Elsevier, vol. 7(2), pages 199-208, August.
    2. 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.
    3. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    4. Franses, P. H., 1990. "Testing For Seasonal Unit Roots In Monthly Data," Papers 272393, Econometric Institute Archives.
    5. Espey, James A. & Espey, Molly, 2004. "Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 36(01), pages 65-81, April.
    6. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
    7. 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.
    8. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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    10. Osterwald-Lenum, Michael, 1992. "A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 461-472, August.
    11. Holtedahl, Pernille & Joutz, Frederick L., 2004. "Residential electricity demand in Taiwan," Energy Economics, Elsevier, vol. 26(2), pages 201-224, March.
    12. Silk, Julian I. & Joutz, Frederick L., 1997. "Short and long-run elasticities in US residential electricity demand: a co-integration approach," Energy Economics, Elsevier, vol. 19(4), pages 493-513, October.
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    Cited by:

    1. Rolando Gonzales Martinez, 2012. "Baysian seasonal analysis with robust priors," Investigación & Desarrollo 0312, Universidad Privada Boliviana, revised Jan 2012.
    2. Matthew Ranson & Lauren Morris & Alex Kats-Rubin, 2014. "Climate Change and Space Heating Energy Demand: A Review of the Literature," NCEE Working Paper Series 201407, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Dec 2014.

    More about this item

    Keywords

    Time series; Econometric models; Residential electricity demand; Error correction Models; Autometrics;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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