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 …

-to-estimate and explain, performs best for forecasting. Our conservative out-of-sample forecast evaluation, using data …

aggregate and individual survey responses in the analysis of expectations and for forecasting. …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

possible to be retrieved with the traditional econometric modelling. Furthermore we examine the forecasting performance of both …

with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than …

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical … literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models … naive random walk in exchange rate forecasting contest …