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 …

an out-of-sample forecasting exercise, the paper shows that the proposed approach performs well as compared to other well …

reflected in the forecasting capabilities of professional analysts: all in all, analysts are not in a position to beat native … environment, we analyse the forecasting behaviour of students experimentally, using a simulated currency series. Our results … indicate that a topically oriented trend adjustment behaviour (TOTA) is a general characteristic of human forecasting behaviour …

Building on the literature on regularization and dimension reduction methods, we have developed a quarterly forecasting …

This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence … Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from AugGVAR models with a … number of data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based methods. It is found …