  Forecasts play a central role in decision making under uncertainty. After a
brief review of the general issues, this paper considers ways of using
high-dimensional data in forecasting. We consider selecting variables from a
known active set, known knowns, using Lasso and OCMT, and approximating
unobserved latent factors, known unknowns, by various means. This combines both
sparse and dense approaches. We demonstrate the various issues involved in
variable selection in a high-dimensional setting with an application to
forecasting UK inflation at different horizons over the period 2020q1-2023q1.
This application shows both the power of parsimonious models and the importance
of allowing for global variables.
