  Forecasters often use common information and hence make common mistakes. We
propose a new approach, Factor Graphical Model (FGM), to forecast combinations
that separates idiosyncratic forecast errors from the common errors. FGM
exploits the factor structure of forecast errors and the sparsity of the
precision matrix of the idiosyncratic errors. We prove the consistency of
forecast combination weights and mean squared forecast error estimated using
FGM, supporting the results with extensive simulations. Empirical applications
to forecasting macroeconomic series shows that forecast combination using FGM
outperforms combined forecasts using equal weights and graphical models without
incorporating factor structure of forecast errors.
