Abstract
Recent literature has shown that electricity demand elasticities may not be constant over time and this has investigated using time-varying estimation methods. As accurate modeling of electricity demand is very important in Azerbaijan, which is a transitional country facing significant change in its economic outlook, we analyze whether the response of electricity demand to income and price is varying over time in this economy. We employed the Time-Varying Coefficient cointegration approach, a cutting-edge time-varying estimation method. We find evidence that income elasticity demonstrates sizeable variation for the period of investigation ranging from 0.48% to 0.56%. The study has some useful policy implications related to the income and price aspects of the electricity consumption in Azerbaijan.
Highlights
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]
We found that income elasticity of electricity demand in Azerbaijan demonstrates sizeable variation for the period of investigation ranging from 0.48 to 0.56, which can be seen as an evidence supporting the objective of the current study
The paper investigates the relationship between electricity demand and economic activity, electricity price using the Time-Varying Coefficient cointegration approach in order to capture the time-variant response of electricity demand to income level, which might be caused due to factors such as rapid economic development, experiencing different economic developmental stages for the period of investigation, shocks in the economy, among others
Summary
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]
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