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Electricity Demand Analysis Using Cointegration and Error-Correction Models with Time Varying Parameters: The Mexican Case

Author

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  • Chang, Yoosoon

    (Rice U)

  • Martinez-Chombo, Eduardo

    (Banco de Mexico)

Abstract

We specify and estimate a double-log functional form of the demand equation, using monthly Mexican electricity data for residential, commercial and industrial sectors. Income, prices and a nonparametric temperature measure are used as explanatory variables, and the income elasticity is allowed to evolve slowly over time by employing the time varying coefficient (TVC) cointegrating model. The specification of the proposed TVC cointegrating model is justified by testing it against the spurious regression and the usual fixed coefficient (FC) cointegration regression. The estimated coefficients suggest that the income elasticity has followed a predominantly increasing path for all sectors during the entire sample period, and that electricity prices do not significantly affect in the long-run the residential and commercial demand for electricity in Mexico.

Suggested Citation

  • 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.
  • Handle: RePEc:ecl:riceco:2003-08
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    References listed on IDEAS

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    1. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, vol. 60(1), pages 119-143, January.
    2. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    8. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    9. Beenstock, Michael & Goldin, Ephraim & Nabot, Dan, 1999. "The demand for electricity in Israel," Energy Economics, Elsevier, vol. 21(2), pages 168-183, April.
    10. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    11. 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.
    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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    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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