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

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Author Info

  • 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.

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File URL: http://www.gwu.edu/~forcpgm/2012-003.pdf
File Function: First version, 2012
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Bibliographic Info

Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2012-003.

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Length: 42 pages
Date of creation: Jan 2012
Date of revision:
Handle: RePEc:gwc:wpaper:2012-003

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Related research

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

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References

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  1. 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.
  2. Robert S. Pindyck, 1979. "The Structure of World Energy Demand," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262661772, December.
  3. 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.
  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. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  6. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  7. Espey, James A. & Espey, Molly, 2004. "Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 36(01), April.
  8. 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 461-72, August.
  9. 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.
  10. 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.
  11. Holtedahl, Pernille & Joutz, Frederick L., 2004. "Residential electricity demand in Taiwan," Energy Economics, Elsevier, vol. 26(2), pages 201-224, March.
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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.

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