Estimating residential demand for electricity in the United States, 1965–2006

Abstract

This paper examines the residential demand for electricity in the US economy as a function of the per capita income, the price of electricity, the price of oil for heating purposes, the weather conditions and the stock of occupied housing over the period 1965-2006. This paper has two novelties: first, the occupied stock of houses as a proxy for the stock of electrical appliances and second the identification of a possible equilibrium relationship among the variables is ascertained through the recently advanced ARDL approach to cointegration. Our empirical findings give support to a stable long-run relationship implying also short-run and long-run elasticities whose size and sign are comparable to other similar studies.

Forecasting residential electricity demand in provincial China

“…In order to capture the impact of previous electricity consumption, 1-year lagged residential electricity consumption is included in the specification. In addition to income, residential electricity consumption can be influenced by a variety of factors, such as electricity price, alternative energy price, urbanization rate, electricity access rate, and heating degree days (Alberini et al 2011;Blázquez et al 2013;Dergiades and Tsoulfidis 2008;Holtedahl and Joutz 2004;Musango 2014). Similar to Schmalensee et al (1998), we include only per capita income in the reduced function, leaving the other explanatory variables uncontrolled for the following reasons.…”

“…Given the continuous improvement of living standards and energy consumption structure adjustment, residential sector will become the main engine of Chinese electricity consumption growth in the foreseeable future. To meet the future demand of electricity Electricity demand forecasting plays a significant role in electricity system planning, energy policy designing, and social stabilization (Dergiades and Tsoulfidis 2008;Son and Kim 2016). Underestimation would result in insufficient generation and unmet demand.…”

Habits and rational behaviour in residential electricity demand

“…More recently, Bernstein and Griffin (2006) and Paul et al (2009) employ dynamic models for energy demand, although they do not address the po- Some recent studies account for dynamic panel bias and use more advanced dynamic panel data models (e.g., panel cointegration, autoregressive distributed-lag (ARDL), generalized method of moments (GMM) estimators) or corrected FE models (e.g., Kiviet (1995) estimator). Dergiades and Tsoulfidis (2008) investigate residential electricity demand in the US between 1965 and 2006 using the ARDL approach to panel cointegration. They estimate a short-run price elasticity of -0.39, and a long-run elasticity of -1.07.…”

Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data

“…The first possible reason is the period over which the elasticity was estimated, which in earlier research has spanned from one year (Krishnamurthy and Kriström, 2013) to over 40 years (Dergiades and Tsoulfidis, 2008). Another is whether over that period the price of electricity was rising or falling.…”

“…review a number of studies, and suggest that differences might be due to the sample period, the nature of the data-such as panels (Maddala et al, 1997;Metcalf and Hasset, 1999;Garcia-Cerrutti, 2000;Bernstein and Griffin, 2005; v. pseudo-panels (Bernard et al, 2011), cross-sections (Nesbakken, 1999;Krishnamurthy and Kriström, 2013;Quigley and Rubinfeld, 1989;Boogen et al, 2014;Reiss and White, 2005;Gans et al, 2013), or time series (Kamerschen andPorter, 2004, or Dergiades andTsoulfidis, 2008)-geography, and level of aggregation of the data. In more recent studies, the price elasticity of electricity consumption ranges from as low as -0.06 (Blazquez et al, 2013) to as high as -1.25 (Krishnamurthy and Kriström, 2013).…”