Revisiting the residential electricity demand in the United States: A dynamic partial adjustment modelling approach

The Price Elasticity of Electricity Demand in the United States: A Three-Dimensional Analysis

“…Our use of a log-log specification follows the standard approach in prior studies (e.g. Shin, 1985;Miller and Alberini, 2016;Ros, 2017;Sun and Yu, 2017). A log-log specification has some attractive properties, including (a) directly providing elasticities, (b) producing residuals with more desirable distributions, 4 (c) ensuring that the predicted values of electricity consumption are always positive, and (d) reducing the potential influence of outliers.…”

“…Prior studies typically seek to identify the long-run price elasticity of electricity demand via dynamic models with lagged dependent variables (e.g. Houthakker, 1980;Dergiades and Tsoulfidis, 2008;Alberini and Filippini, 2011;Ros, 2017;Sun and Yu, 2017). These studies back-out an implied long-run multiplier based on forward simulations of their model, using a formula that divides the short-run multiplier by 1-ψ, where ψ is the coefficient on the lagged dependent variable.…”

“…Miller and Alberini (2016) obtained a residential price elasticity of electricity demand of around -0.7 in their state-level panel estimate without state fixed effects for a dataset that extends to 2009. 1 Using state-level panel data to 2010, Sun and Yu (2017) found short-and long-run price elasticities of residential electricity demand of -0.1 and up to -1.0, respectively. Our results add to a consensus that the short-run price elasticity of aggregate residential electricity demand is around -0.1 and the long-run elasticity is substantially larger.…”

Analysis of household LPG demand elasticity in Cameroon and policy implications

“…The inclusion of lagged dependent variables increases the amount of data needed, but it also offers an easy method to take into account past variables that result in current changes in the dependent variable and are challenging to take into account in other methods. This model provides a lot of benefits especially when the series are integrated of order 1, I (1), as it then allows us to estimate both short- and long-run relations [ 43 ].…”

Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach

“…The response of electricity demand to economic variables has been widely investigated in the literature, with numerous econometric techniques, parametric [1,2] and non-parametric [3] ranging from aggregate national time series [4,5] country panels [6,7] to regional and household micro data [8,9]. Results have shown a variety of empirical quantifications for price and income elasticities, in the short and in the long run (e.g., [1,10,11]).…”