Econometric Estimation of Energy Demand Elasticities
Project duration: 7/2008 - 6/2011
Funded by E.ON ERC
Energy-related policy designs have far-reaching and longterm consequences for the structure of the prevailing energy system. Hence, awareness of the mechanisms underlying energy markets is important. Accurate prediction of how energy consumers react to changes in prices, income and other explanatory variables facilitates understanding of the functioning of energy markets. This understanding, in turn, provides guidance for policy decisions that, hopefully, results in a future where energy is used in a more environmentally and socially benign fashion.
The methodological focus of the project lies in the application of time series and panel data econometric methods. We employ three cointegration techniques, each having its own merits: (1) the maximum likelihood system approach by Johansen, (2) the fully modified OLS (FMOLS) and dynamic OLS (DOLS) group-means panel estimation framework by Pedroni, and (3) the autoregressive distributed lag (ARDL) bounds testing procedure by Pesaran.
In the first part, we estimate industrial electricity demand elasticities at the subsectoral level. The annual data set used covers eight subsectors of the German economy. By employing a cointegrated VAR model specification we find cointegration relationships for five of the eight subsectors studied. The long-run demand elasticities range between 0.7 and 1.9 for economic activity and between –0.5 and zero for the price of electricity. The short-run elasticities are estimated by single-equation error-correction modeling (ECM) and found to be between 0.2 to 1.0 for economic activity and –0.6 to zero for electricity price.
In the second part, we estimate residential electricity demand elasticities and conduct an analysis of the causal relationship between electricity demand, disposable income and electricity price for a group of eighteen OECD members by applying panel cointegration methods. Our results for the whole panel indicate a near unity income elasticity and an inelastic price elasticity of approximately –0.4 in the long run. These results are robust with regard to the estimation methods employed (group-means panel FMOLS and DOLS). In the short run, our estimates from an ECM indicate an income elasticity of 0.2 and a price elasticity of approximately –0.1.
In the third part, we analyze residential natural gas demand for twelve OECD countries using time series data from 1980 to 2008. We estimate long-run demand elasticities with regard to real disposable income and real residential natural gas price using the ARDL bounds testing procedure. By employing an error correction framework we also obtain estimates of the speeds of adjustment to long-run equilibria and short-run elasticities for individual countries. We account for the effect of weather conditions on natural gas demand in a given year by including heating degree days as a control variable. On average, the long-run elasticities are 0.9 with regard to income, –0.5 with regard to price and 1.4 with regard to heating degree days. The short-run dynamics, assessed by estimation of the error correction models, indicate an average adjustment coefficient of –0.6, a shortrun income elasticity of 0.5, a short-run price elasticity of –0.2 and a short-run elasticity with regard to heating degree days of 0.7.
Overall, regardless of the sector or energy type considered, our results imply that the steering effect of tax-induced price increases on energy demand has a very limited potential for energy conservation, and hence a reduction of GHG emissions.
Bernstein R., Madlener R. (2010). Impact of Disaggregated ICT Capital on Electricity Intensity in European Manufacturing, Applied Economics Letters, 17(17): 1691-1695.
Bernstein R., Madlener R. (2010). Short- and Long-Run Electricity Demand Elasticities at the Subsectoral Level: A Cointegration Analysis for German Manufacturing Industries, FCN Working Paper No. 19/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Bernstein R., Madlener R. (2011). Responsiveness of Residential Electricity Demand in OECD Countries: A Panel Cointegration and Causality Analysis, FCN Working Paper No. 8/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Bernstein R., Madlener R. (2011). Residential Natural Gas Demand Elasticities in OECD Countries: An ARDL Bounds Testing Approach, FCN Working Paper No. 15/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Madlener R., Bernstein R., Alva González M.Á. (2011). Econometric Estimation of Energy Demand Elasticities, E.ON Energy Research Center Series, Vol. 3, Issue 8, October (ISSN: 1868-7415). [Download]
Supervised student research (selection)
Schäfer A. (2008). Analysis of the Demand for Electricity and its Expected Future Development in the Commercial and Services Sector until 2035 from a European Perspective, Study thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University.
Ruhrmann J. (2012). Literaturüberblick zum Thema Energienachfrage-Elastizitäten: Modelle, Schätzmethoden und Resultate, Study thesis, Chair of Energy Economics and Managment, School of Business and Economics, RWTH Aachen University.