able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are … observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and … autoregression residuals, are somewhat able to provide better forecasting results than simple linear models. Thus, it may be …

forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor … on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset …

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 …

duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi … estimators. We provide simulation and forecasting evidence that price duration estimators can extract relevant information from …-implied variance estimators, when considered in isolation or as part of a forecasting combination setting …