This paper discusses identification, specification, estimation and forecasting for a general class of periodic … formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues …

modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM …

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 …

projections shows a forecasting accuracy of 99% …

models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M, Taylor-Schwert GARCH, GJR …-normally distributed, while GARCH-M does the best in the group of normally distributed …

deviation variables based off the I-Flow time series with a 5-day lag to the one-period I-Flow forecasting model to discover a … forecasting system; The first model involving volatility trades for the FX pair that could exploit an expected upswing in the … short term volatility of the FX pair, and the second model involving an overlay of the swing forecasting system over …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …