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This paper examines empirical methods for estimating the response ofaggregated electricity demand to high-frequency price signals, the short-termelasticity of electricity demand. We investigate how the endogeneity of pricesand the autocorrelation of the time series, which are particularly pronouncedat hourly granularity, affect and distort common estimators. After developing acontrolled test environment with synthetic data that replicate key statisticalproperties of electricity demand, we show that not only the ordinary leastsquare (OLS) estimator is inconsistent (due to simultaneity), but so is aregular instrumental variable (IV) regression (due to autocorrelation). Usingwind as an instrument, as it is commonly done, may result in an estimate of thedemand elasticity that is inflated by an order of magnitude. We visualize thereason for the Thams bias using causal graphs and show that its magnitudedepends on the autocorrelation of both the instrument, and the dependentvariable. We further incorporate and adapt two extensions of the IV estimation,conditional IV and nuisance IV, which have recently been proposed by Thams etal. (2022). We show that these extensions can identify the true short-termelasticity in a synthetic setting and are thus particularly promising forfuture empirical research in this field.

Price elasticity of electricity demand: Using instrumental variable  regressions to address endogeneity and autocorrelation of high-frequency time  series