  This paper develops a novel, fully automated forecast averaging scheme, which
combines LASSO estimation method 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' at hock
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
almost two and a half year of out-of-sample period and compared to other semi-
and fully automated methods, such as simple mean, AW/WAW, LASSO and PCA. The
results indicate that the LASSO averaging is very efficient in terms of
forecast error reduction, whereas PCA method is robust to the selection of the
specification parameter. LPCA inherits the advantages of both methods and
outperforms other approaches in terms of MAE, remaining insensitive the the
choice of a tuning parameter.
