Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We examine possible accuracy gains from using factor models, quantile regression and forecast averaging to compute interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014a), and use principal component analysis to automate the process of selecting from among a large set of individual forecasting models that are available for averaging. We show that the resulting Factor Quantile Regression Averaging (FQRA) approach performs very well for price (and load) data from the British power market. In terms of unconditional coverage, conditional coverage and the Winkler score, we find the FQRA-implied prediction intervals to be more accurate than those of either the benchmark ARX model or the QRA approach.