A0734
Title: Functional factor regression with an application to electricity price curve modeling
Authors: Luis Winter - University of Cologne (Germany) [presenting]
Sven Otto - University of Cologne (Germany)
Abstract: A function-on-function linear regression model is proposed for time-dependent curve data that is consistently estimated by imposing factor structures on the regressors. An integral operator based on cross-covariances identifies two components for each functional regressor: A predictive low-dimensional component, along with associated factors that are guaranteed to be correlated with the dependent variable, and an infinite-dimensional component that has no predictive power. In order to consistently estimate the correct number of factors for each regressor, a functional eigenvalue difference test is introduced. While conventional estimators for functional linear models fail to converge in distribution, asymptotic normality is established, making it possible to construct confidence bands and conduct statistical inference. The model is applied to forecast electricity price curves in three different energy markets. Its prediction accuracy is found to be comparable to popular machine learning approaches while providing statistically valid inference and interpretable insights into the conditional correlation structures of electricity prices.
