An Error Correction Model for Forecasting Philippine Aggregate Electricity Consumption

Danao, Rolando and Ducanes, Geoffrey (2016): An Error Correction Model for Forecasting Philippine Aggregate Electricity Consumption. Forthcoming in: Electricity Policy in the Philippines: Generation, Institutions, and Prices No. Stand-alone book to be published by the University of the Philippine Press (2019)

Abstract

This paper presents an error correction model for forecasting electricity consumption in the Philippines based on income, price, and temperature. The empirical evidence shows that there is a long-run positive and inelastic relationship between electricity consumption and income. We find that income, price, and temperature have significant short-run effects. Short-run demand is positive and inelastic with respect to income, negative and inelastic with respect to price, and positive and elastic with respect to temperature. Despite the small sample size, the model passes the standard diagnostic and parameter stability tests and performs well in within-sample and out-of-sample forecasting. It can be used not only for forecasting but also for analyzing, through simulations, the impacts on electricity consumption of changes in income, price, and temperature.

The simulations confirm that, in the long run, electricity consumption is mainly driven by economic growth. Increasing GDP growth rate from 6% per year to 7% could increase electricity consumption at the end of 15 years by 10%. Although the effect of electricity price on electricity consumption is small (because of low price elasticity in absolute terms) and the effect of temperature change is also small (because annual average temperature change is small), their combined effects could add up and our simulation indicates that under very conservative assumptions, electricity consumption at the end of 15 years could rise further by 2%. Thus, it is important to include these variables in the simulations in order to account for their combined effects.

• Item Type: MPRA Paper
• Language: English
• Keywords: Electricity consumption, forecasting, error correction model
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy

References

Amarawickrama, H. A. and Hunt, L. C. [2008], Electricity demand for Sri Lanka: a time series analysis, Energy, 33: 724-739.

Banerjee, A., Dolado, J. J., Galbraith, J. W., and Hendry, D. F. [1993], Cointegration, error-correction, and the econometric analysis of nonstationary data, Advanced Texts in Econometrics, Oxford University Press

Berndt, E. R. [1984], Modeling the aggregate demand for electricity: simplicity vs. virtuosity, in J. R. Moroney, ed., Advances in the Economics of Energy and Resources, vol. 5, Greenwich, Conn.: JAI Press, 141-152.

Bewley, R. A. [1979], The direct estimation of the equilibrium response in a linear dynamic model, Economics Letters, vol 3, 357-361.

Bianco, V., Manca, O., Nardini, S., and Minea, A. A. [2010], Analysis and forecasting of nonresidential electricity consumption in Romania, Applied Energy 87, 3584-3590.

Cinco, T. A., Hilario, F. D., De Guzman, R. G., and Ares, E. D., [2013], Climate Trends and Projections in the Philippines, paper presented at the 12th National Convention on Statistics, October 1-2, 2013.

Dickey, D. A. and Fuller, W. A. [1979], Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association 74, 427-431.

Enders, W. [2010], Applied Econometric Time Series, Wiley, Hoboken, N.J.

Engle, R. F. and C. W. J. Granger [1987], Cointegration and error correction: representation, estimation, and testing, Econometrica 55, 251-76.

Galindo, L. M. [2005], Short- and long-run demand for energy in Mexico: a cointegration approach, Energy Policy 33, 1179-1185.

Goel, Aayush and Goel, Agam [2014], Regression Based Forecast of Electricity Demand of New Delhi, International Journal of Scientific and Research Publications, Vol. 4, Issue 9, 1-7.

Hansen, B. E. [1992], Tests for parameter stability in regressions with I(1) processes, Journal of Business and Economic Statistics 10, 321-335.

Harris, R. I. D. [1995], Using Cointegration Analysis in Econometric Modelling, Prentice-Hall, London.

Hunt, L. C. and Manning, N. [1989], Energy price- and income-elasticities of demand: some estimates for the UK using cointegration procedure. Scott J Pol Econ, 36(2): 183-93.

Inder, B. [1993], Estimating long-run relationship in economics: a comparison of different approaches, J. Econ., 57(1-3), 53-68.

Kennedy, P. [2003], A Guide to Econometrics, The MIT Press, Mass.

Kremers, J. J. M., Ericsson, N. R., and Dolado, J. J. [1992], The Power of Cointegration Tests, Discussion Paper # 431, International Finance, Board of Governors of the Federal Reserve System.

Lam, J. C., Tang, H. L. and Li, D. H. W. [2008], Seasonal variations in residential and commercial electricity consumption in Hong Kong, Energy 33, 513-523.

Phillips, P. C. B. and B. E. Hansen [1990], Statistical inference in instrumental variables regression with I(1) processes, Review of Economic Studies 57, 99-125.

Philippine Power Statistics [2014], Department of Energy Portal

PSY [2014], Philippine Statistical Yearbook, 2014

Taylor, L. D. [1975], The demand for electricity: a survey, Bell Journal of Economics and Management Science, 6:1, Spring, 74-110.

Van Helden, G.J.,Leeflang, P.S.H., and Sterken, E. [1987], Estimation of the demand for electricity, Applied Economics, 19: 69-82.

Vogelvang, B. [2005], Econometrics: Theory and Applications with EViews, Addison-Wesley, Harlow, England.

Zachariadis, T., [2010], Forecast of electricity consumption in Cyprus up to the year 2030: The potential impact of climate change, Energy Policy 38, 744-750.