It is widely accepted that long-run elasticities of demand for electricity are not stable over time. We model long-run sectoral electricity demand using a time-varying cointegrating vector. Specifically, the coefficient on income (residential sector) or output (commercial and industrial sectors) is allowed to follow a smooth semiparametric function of time, providing a flexible specification that allows more accurate out-of-sample forecasts than either fixed or discretely changing regression coefficients.
We show how temporal aggregation affects the size and power of the DOLS residual-based KPSS test of the null of cointegration. Size is effectively controlled by setting the minimum number of leads equal to one -- as opposed to zero -- when selecting the lag/lead order of the DOLS regression, but at a cost to power in finite samples. If high-frequency data for one or more series are available, we show how to effectively utilize the high-frequency data to increase power while controlling size.