IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Time-varying Long-run Income and Output Elasticities of Electricity Demand

It is widely accepted that long-run elasticities of demand for electricity are not stable over time. We model long-run sectoral electricity demand using a time-varying cointegrating vector. Specifically, the coefficient on income (residential sector) or output (commercial and industrial sectors) is allowed to follow a smooth semiparametric function of time, providing a flexible specification that allows more accurate out-of-sample forecasts than either fixed or discretely changing regression coefficients. We fit the model to Korean data over 1995:01-2012:12 for the residential sector and 1985:01-2012:12 for the commercial and industrial sectors. The rapid development of Korea over this period provides a very clear case for allowing the coefficient on income/output to vary over time, but the essential modeling strategy is widely applicable.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://economics.missouri.edu/working-papers/2014/WP1409_miller.pdf
Download Restriction: no

Paper provided by Department of Economics, University of Missouri in its series Working Papers with number 1409.

as
in new window

Length: 37 pgs.
Date of creation: 03 Jun 2014
Date of revision:
Publication status: Published in Energy Economics 2014
Handle: RePEc:umc:wpaper:1409
Contact details of provider: Postal: 118 Professional Building, Columbia, MO 65211
Phone: (573) 882-0063
Fax: (573) 882-2697
Web page: http://economics.missouri.edu/

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Rossana Galli, 1998. "The Relationship Between Energy Intensity and Income Levels: Forecasting Long Term Energy Demand in Asian Emerging Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 85-105.
  2. repec:mcb:jmoncb:v:45:y:2013:i::p:933-952 is not listed on IDEAS
  3. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, June.
  4. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
  5. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
  6. Moral-Carcedo, Julian & Vicens-Otero, Jose, 2005. "Modelling the non-linear response of Spanish electricity demand to temperature variations," Energy Economics, Elsevier, vol. 27(3), pages 477-494, May.
  7. Halvorsen, Robert, 1975. "Residential Demand for Electric Energy," The Review of Economics and Statistics, MIT Press, vol. 57(1), pages 12-18, February.
  8. Jonathan E. Hughes & Christopher R. Knittel & Daniel Sperling, 2008. "Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand," The Energy Journal, International Association for Energy Economics, vol. 29(1), pages 113-134.
  9. J. Isaac Miller & Ronald Ratti, 2008. "Crude Oil and Stock Markets: Stability, Instability, and Bubbles," Working Papers 0810, Department of Economics, University of Missouri, revised 20 Jan 2009.
  10. Bernstein, Ronald & Madlener, Reinhard, 2010. "Short- and Long-Run Electricity Demand Elasticities at the Subsectoral Level: A Cointegration Analysis for German Manufacturing Industries," FCN Working Papers 19/2010, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
  11. Eric Ghysels & J. Isaac Miller, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," Working Papers 1307, Department of Economics, University of Missouri, revised 07 May 2014.
  12. Chen, Li-Hsueh & Finney, Miles & Lai, Kon S., 2005. "A threshold cointegration analysis of asymmetric price transmission from crude oil to gasoline prices," Economics Letters, Elsevier, vol. 89(2), pages 233-239, November.
  13. Beenstock, Michael & Goldin, Ephraim & Nabot, Dan, 1999. "The demand for electricity in Israel," Energy Economics, Elsevier, vol. 21(2), pages 168-183, April.
  14. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
  15. Bentzen, Jan & Engsted, Tom, 1993. "Short- and long-run elasticities in energy demand : A cointegration approach," Energy Economics, Elsevier, vol. 15(1), pages 9-16, January.
  16. Lutz Kilian & Robert J. Vigfusson, 2011. "Are the responses of the U.S. economy asymmetric in energy price increases and decreases?," Quantitative Economics, Econometric Society, vol. 2(3), pages 419-453, November.
  17. Olutomi I Adeyemi & Lester C Hunt, 2006. "Modelling OECD Industrial Energy Demand: Asymmetric Price Responses and Energy – Saving Technical Change," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 115, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  18. 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.
  19. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
  20. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, vol. 60(1), pages 119-43, January.
  21. Park, Sung Y. & Zhao, Guochang, 2010. "An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach," Energy Economics, Elsevier, vol. 32(1), pages 110-120, January.
  22. Hinich Melvin J. & Serletis Apostolos, 2006. "Randomly Modulated Periodic Signals in Alberta's Electricity Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-15, September.
  23. Halvorsen, Bente & Larsen, Bodil M., 2001. "The flexibility of household electricity demand over time," Resource and Energy Economics, Elsevier, vol. 23(1), pages 1-18, January.
  24. Berndt, Ernst R & Wood, David O, 1975. "Technology, Prices, and the Derived Demand for Energy," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 259-68, August.
  25. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-62, February.
  26. Chang-Jin Kim & Cheolbeom Park, 2012. "Disappearing Dividends: Implications for the Dividend-Price Ratio and Return Predictability," Discussion Paper Series 1205, Institute of Economic Research, Korea University.
  27. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
  28. Jordi Galí & Mark J. Gertler, 2010. "International Dimensions of Monetary Policy," NBER Books, National Bureau of Economic Research, Inc, number gert07-1, June.
  29. Haas, Reinhard & Schipper, Lee, 1998. "Residential energy demand in OECD-countries and the role of irreversible efficiency improvements," Energy Economics, Elsevier, vol. 20(4), pages 421-442, September.
  30. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
  31. Park, Joon Y. & Shin, Kwanho & Whang, Yoon-Jae, 2010. "A semiparametric cointegrating regression: Investigating the effects of age distributions on consumption and saving," Journal of Econometrics, Elsevier, vol. 157(1), pages 165-178, July.
  32. Yoosoon Chang & Yongok Choi & Chang Sik Kim & Joon Y. Park & J. Isaac Miller, 2013. "Disentangling Temporal Patterns in Elasticities: A Functional Coefficient Panel Analysis of Electricity Demand," Working Papers 1320, Department of Economics, University of Missouri.
  33. Engle, R. F. & Granger, C. W. J. & Hallman, J. J., 1989. "Merging short-and long-run forecasts : An application of seasonal cointegration to monthly electricity sales forecasting," Journal of Econometrics, Elsevier, vol. 40(1), pages 45-62, January.
  34. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(05), pages 664-703, October.
  35. Cheolbeom Park, 2011. "How does changing age distribution impact stock prices? a nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 886-887, 08.
  36. Kenneth B. Medlock III & Ronald Soligo, 2001. "Economic Development and End-Use Energy Demand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 77-105.
  37. Ruth A. Judson & Richard Schmalensee & Thomas M. Stoker, 1999. "Economic Development and the Structure of the Demand for Commercial Energy," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 29-57.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:umc:wpaper:1409. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Valerie Kulp)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.