  This paper proposes a new Bayesian machine learning model that can be applied
to large datasets arising in macroeconomics. Our framework sums over many
simple two-component location mixtures. The transition between components is
determined by a logistic function that depends on a single threshold variable
and two hyperparameters. Each of these individual models only accounts for a
minor portion of the variation in the endogenous variables. But many of them
are capable of capturing arbitrary nonlinear conditional mean relations.
Conjugate priors enable fast and efficient inference. In simulations, we show
that our approach produces accurate point and density forecasts. In a real-data
exercise, we forecast US macroeconomic aggregates and consider the nonlinear
effects of financial shocks in a large-scale nonlinear VAR.
