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

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 …

This paper presents an exchange rate forecasting model which combines the multi-state Markov-switching model with …

statistical benchmarks, and revisit the forecasting performance of changes in commodity currencies as efficient predictors of … least squares (PLS) regression to extract dynamic factors from the data set. Our forecasting analysis considers ten …

key issue is this: In the literature on developing forecasting models, new models are put together based on the results … variety of exercises designed to answer this question. In particular, we find that real-time data matters for some forecasting …

indicators for forecasting quarterly Chinese GDP growth. We iterate the evaluation over forecast horizons from 370 days to 1 day … 2009. Industrial production can be quite valuable for now- or even forecasting, but only if it is released shortly after … manufacturing purchasing managers' index of the Chinese National Bureau of Statistics help much for now- or forecasting. Our results …