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Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea

Author

Listed:
  • Chang, Yoosoon
  • Kim, Chang Sik
  • Miller, J. Isaac
  • Park, Joon Y.
  • Park, Sungkeun

Abstract

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.

Suggested Citation

  • Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    • Handle: RePEc:eee:eneeco:v:46:y:2014:i:c:p:334-347
    DOI: 10.1016/j.eneco.2014.10.003
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    as
    1. 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.
    2. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, vol. 60(1), pages 119-143, January.
    3. Chang, Yoosoon & Martinez-Chombo, Eduardo, 2003. "Electricity Demand Analysis Using Cointegration and Error-Correction Models with Time Varying Parameters: The Mexican Case," Working Papers 2003-08, Rice University, Department of Economics.
    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. 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.
    6. 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.
    7. 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 259-268, August.
    8. repec:aen:journl:2008v29-01-a06 is not listed on IDEAS
    9. Apostolos Serletis, 2012. "Interfuel Substitution in the United States," World Scientific Book Chapters, in: Interfuel Substitution, chapter 2, pages 11-35, World Scientific Publishing Co. Pte. Ltd..
    10. repec:mcb:jmoncb:v:45:y:2013:i::p:933-952 is not listed on IDEAS
    11. Inglesi-Lotz, R., 2011. "The evolution of price elasticity of electricity demand in South Africa: A Kalman filter application," Energy Policy, Elsevier, vol. 39(6), pages 3690-3696, June.
    12. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(5), pages 664-703, October.
    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. 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.
    15. 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.
    16. Milton Friedman, 1962. "Introduction to "The Interpolation of Time Series by Related Series"," NBER Chapters, in: The Interpolation of Time Series by Related Series, pages 1-3, National Bureau of Economic Research, Inc.
    17. Halvorsen, Robert, 1975. "Residential Demand for Electric Energy," The Review of Economics and Statistics, MIT Press, vol. 57(1), pages 12-18, February.
    18. repec:aen:journl:2010v31-04-a01 is not listed on IDEAS
    19. Serletis, Apostolos & Shahmoradi, Asghar, 2008. "Semi-nonparametric estimates of interfuel substitution in U.S. energy demand," Energy Economics, Elsevier, vol. 30(5), pages 2123-2133, September.
    20. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2016. "Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 232-243.
    21. 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.
    22. Miller, J. Isaac & Ratti, Ronald A., 2009. "Crude oil and stock markets: Stability, instability, and bubbles," Energy Economics, Elsevier, vol. 31(4), pages 559-568, July.
    23. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, August.
    24. 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.
    25. 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.
    26. Fan, Shu & Hyndman, Rob J., 2011. "The price elasticity of electricity demand in South Australia," Energy Policy, Elsevier, vol. 39(6), pages 3709-3719, June.
    27. Serletis, Apostolos & Timilsina, Govinda & Vasetsky, Olexandr, 2011. "International evidence on aggregate short-run and long-run interfuel substitution," Energy Economics, Elsevier, vol. 33(2), pages 209-216, March.
    28. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    29. 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 149-162, February.
    30. 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.
    31. repec:aen:journl:2001v22-02-a04 is not listed on IDEAS
    32. 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.
    33. repec:aen:journl:1998v19-04-a04 is not listed on IDEAS
    34. Bernstein, Ronald & Madlener, Reinhard, 2015. "Short- and long-run electricity demand elasticities at the subsectoral level: A cointegration analysis for German manufacturing industries," Energy Economics, Elsevier, vol. 48(C), pages 178-187.
    35. repec:aen:journl:2002v23-01-a02 is not listed on IDEAS
    36. repec:aen:journl:1999v20-02-a02 is not listed on IDEAS
    37. Adeyemi, Olutomi I. & Hunt, Lester C., 2007. "Modelling OECD industrial energy demand: Asymmetric price responses and energy-saving technical change," Energy Economics, Elsevier, vol. 29(4), pages 693-709, July.
    38. 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, August.
    39. 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.
    40. 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.
    41. Chang‐Jin Kim & Cheolbeom Park, 2013. "Disappearing Dividends: Implications for the Dividend–Price Ratio and Return Predictability," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(5), pages 933-952, August.
    42. 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.
    43. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    44. Train, Kenneth & Ignelzi, Patrice & Engle, Robert & Granger, Clive & Ramanathan, Ramu, 1984. "The billing cycle and weather variables in models of electricity sales," Energy, Elsevier, vol. 9(11), pages 1041-1047.
    45. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-247, February.
    46. Dongfeng Chang & Apostolos Serletis, 2014. "The Demand For Gasoline: Evidence From Household Survey Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 291-313, March.
    47. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 14, pages 93-122, Emerald Group Publishing Limited.
    48. repec:dau:papers:123456789/8180 is not listed on IDEAS
    49. 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.
    50. Jordi Galí & Mark J. Gertler, 2010. "International Dimensions of Monetary Policy," NBER Books, National Bureau of Economic Research, Inc, number gert07-1, August.
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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