Bilgili, Faik and Pamuk, Yalçın and Halıcı Tülüce, Nadide Sevil (2010): Short run and long run dynamics of residential electricity consumption: Homogeneous and heterogeneous panel estimations for OECD. Published in: Economic Computation and Economic Cybernetics Studies and Research No. 3/2011 (August 2011): pp. 113-126.
Download (281kB) | Preview
The purpose of this paper is to reveal the short run and long run dynamics of residential electricity consumption for 11 OECD countries within annual period 1979-2006. To this end, this paper first explores the findings from related literature evidence and, later, follows panel cointegration equations (CEs) and panel error correction models (ECMs). CEs give long run relations of the variables in residential electricity demand function. ECMs include both long run and short run parameter estimates of the per capita residential electricity demand in terms of residential electricity price, residential light fuel oil price, residential natural gas price and per capita income. For both ECs and ECMs, the techniques of panel OLS, panel adjusted OLS and panel dynamic OLS are utilized. Finally, this paper yields short term and long term elasticities of residential electricity consumption together with error correction terms through homogeneous and heterogeneous variance structures.
|Item Type:||MPRA Paper|
|Original Title:||Short run and long run dynamics of residential electricity consumption: Homogeneous and heterogeneous panel estimations for OECD|
|Keywords:||electricity consumption, elasticities, homogeneous and heterogeneous variance structures, panel error correction model, panel dynamic ordinary least squares|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C33 - Models with Panel Data; Longitudinal Data; Spatial Time Series
Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy
|Depositing User:||Faik Bilgili|
|Date Deposited:||10. Sep 2011 14:51|
|Last Modified:||15. Feb 2013 04:14|
 Al-Iriani, M.A. (2004), Climate-related Electricity Demand-side Management in Oil-exporting Countries, the Case of the United Arab Emirates. Energy Policy, 33, 2350–2360.
 Aldea, A., Ciobanu, A. (2011), Analysis of Renewable Energy Development Using Bootstrap Efficiency Estimates. Journal of Economic Computation And Economic Cybernetics Studies And Research, 1, 77-90.  Athukorala P. P. A. W., Wilson, C. (2009), Estimating Short and Long-term Residential Demand for Electricity: New Evidence from Sri Lanka. Energy Economics, article in press.
 Breitung, J. (2000), The Local Power of Some Unit Root Tests for Panel Data. Advances in Econometrics, 15, 161-178.
 Chiang, M. H., Kao, C. (2002), Nonstationarity Panel Time Series Using NPT 1.3 – A User Guide. National Cheng-Kung University and Syracuse University.
 Dergiades, T., Tsoulfidis, L. (2008), Estimating Residential Demand for Electricity in the United States, 1965–2006. Energy Economics, 30, 2722–2730.
 Filippini, M., Pachauri, S. (2004), Elasticities of Electricity Demand in Urban Indian Households. Energy Policy, 32, 429–436.
 Hadri, K. (2000), Testing for Stationarity in Heterogeneous Panel Data. Econometric Journal, 3, 148–161.
 Heston, A., Summers, R., Aten, B. (2009), Penn World Table Version 6.3, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.
 Hondroyiannis, G. (2004), Estimating Residential Demand for Electricity in Greece. Energy Economics, 26, 319– 334.
 IEA, International Energy Agency (2008). Energy end-use prices, 2Q2008 documentation of IEA, Energy prices and Taxes: Beyond 2020.
 Im, K. S., Pesaran, M. H., Shin, Y. (2003), Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 115, 53-74.
 Inglesi, R. (2010), Aggregate Electricity Demand in South Africa: Conditional Forecasts to 2030. Applied Energy, 87, 197–204.
 Kao, C., Chiang, M. H. (2000), On the Estimation and Inference of a Cointegrated Regression in Panel Data. Nonstationarity Panels, Panel Cointegration and Dynamic Panels, Elsevier Science Inc. ISBN: 0–7623–0688–2, Volume 15, 179–222.
 Kao, C. (1999), Spurious Regression and Residual-based Tests for Cointegration in Panel Data. Journal of Econometrics, 90, 1–44.  Lai, Y-H., Wang, K-M., Chen, T-W. (2011), The Asymmetric Dependence Structure Between Oil And Stock Prices. Journal of Economic Computation And Economic Cybernetics Studies And Research, 2, 201-222.
 Mitchell, T. (2006) , A Co-integration Analysis of the Price and Income Elasticity of Energy Demand. Research Department, Central Bank of Barbados, Bridgetown, Barbados.
 Nakajima, T., Hamori, S. (2010), Change in Consumer Sensitivity to Electricity Prices in Response to Retail Deregulation: A Panel Empirical Analysis of the Residential Demand for Electricity in the United States. Energy Policy, 38, 470–2476.
 Narayan, P. K., Smyth, R., Prasad, A. (2007), Electricity Consumption in G7 Countries: A Panel Cointegration Analysis of Residential Demand Elasticities. Energy Policy, 35, 4485–4494.
 Pedroni, P. (2004), Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econometric Theory, 20, 597-625.
 Pedroni, P. (2001), Purchasing Power Parity Tests in Cointegrated Panels. The Review of Economics and Statistics, 83, 727–731.
 Pedroni, P. (1999), Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics, 61, 653–670.
 Pedroni, P. (1995), Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Manuscript, Indiana University, Department of Economics.
 Sa’ad, S. (2009), Electricity Demand for South Korean Residential Sector. Energy Policy, 37, 5469–5474.