LASSO principal component averaging: A fully automated approach for point forecast pooling

This paper develops a novel, fully automated forecast averaging scheme which combines LASSO estimation with principal component averaging (PCA). LASSO-PCA (LPCA) explores a pool of predictions based on a single model but calibrated to windows of different sizes. It uses information criteria to select tuning parameters and hence reduces the impact of researchers' ad hoc decisions. The method is applied to average predictions of hourly day-ahead electricity prices over 650 point forecasts obtained with various lengths of calibration windows. It is evaluated on four European and American markets with an out-of-sample period of almost two and a half years and compared to other semi- and fully automated methods, such as the simple mean, AW/WAW, LASSO, and PCA. The results indicate that LASSO averaging is very efficient in terms of forecast error reduction, whereas PCA is robust to the selection of the specification parameter. LPCA inherits the advantages of both methods and outperforms other approaches in terms of the mean absolute error, remaining insensitive to the choice of a tuning parameter. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).