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 …

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

’ profitability forecasting, techniques, and most appropriate models to improve the correctness of predicting and acquiring more … forecasting. It conducts investigations for the relevant studies, using regression analysis, necessary tests, ascertains …

should be taken as a lower bound, results show a better WBM forecasting ability than the benchmark case and confirm the … show that the (unrealistic) use of revised data leads to a systematic overstatement of model forecasting performance …

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

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 …