Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and … proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which … powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting …

encouraging. In a pseudo out-of-sample exercise, our approach beats relevant benchmarks for forecasting CPI inflation and an …

pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE …

We extend the literature on economic forecasting by constructing a mixed-frequency time-varying parameter vector … competitors. In particular, inflation forecasts benefit from this new forecasting approach. Finally, we assess the models …