  This paper examines empirical methods for estimating the response of
aggregated electricity demand to high-frequency price signals, the short-term
elasticity of electricity demand. We investigate how the endogeneity of prices
and the autocorrelation of the time series, which are particularly pronounced
at hourly granularity, affect and distort common estimators. After developing a
controlled test environment with synthetic data that replicate key statistical
properties of electricity demand, we show that not only the ordinary least
square (OLS) estimator is inconsistent (due to simultaneity), but so is a
regular instrumental variable (IV) regression (due to autocorrelation). Using
wind as an instrument, as it is commonly done, may result in an estimate of the
demand elasticity that is inflated by an order of magnitude. We visualize the
reason for the Thams bias using causal graphs and show that its magnitude
depends on the autocorrelation of both the instrument, and the dependent
variable. We further incorporate and adapt two extensions of the IV estimation,
conditional IV and nuisance IV, which have recently been proposed by Thams et
al. (2022). We show that these extensions can identify the true short-term
elasticity in a synthetic setting and are thus particularly promising for
future empirical research in this field.
