We provide an empirical analysis of the French electricity market.
Specifically, we look at the impact of uncertainty on supplier
strategies and take this as evidence that dynamic costs matter. Our
approach to separate out the uncertainty from market demand
expectations and predictability of renewables generation is novel.
Both proxies for uncertainty used are new, uncertainty from market
demand is inferred from the prediction errors that firms make in a
demand estimation and uncertainty from renewable production is
computed in a bottom-up approach from local weather forecasts. Instead
of opting for a time series regression, we understand all hourly
auctions as a cross-sectional dataset and control for the time of the
day by using continuous transition variables for daytime periods.
Similarly, we control for seasonality using continuous variables
rather than dummies. Thereby, we are able to leverage our dataset and
increase the sample size for each of our regression and improve the
precision of our estimates.