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 …

…ndings provide an easy-to-use method for conducting mixed data-sampling analysis as well as for forecasting world commodity price …

This paper reviews the main monthly indicators that could help forecasting world trade and compares different type of … forecasting models using these indicators. In particular it develops dynamic factor models (DFM) which have the advantage of … and information technology indices. The comparison of the forecasting performance of the DFMs with more traditional bridge …

the random walk model, suggests that the forecasting performance of the monetary model is superior. …

markets. We analyze the respective forecasting accuracy and our results indicate that there exist substantial differences …

reflected in the forecasting capabilities of professional analysts: all in all, analysts are not in a position to beat naïve … 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 …

Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only … forecasting horizons. …