as a pseudo real time forecasting exercise, i.e. due account is taken of the pattern of available monthly variables over …

Indices (PMI) in anticipating US real economic activity. We conduct a fully-fledged real-time out-ofsample forecasting … forecasting GDP growth, while it performs quite poorly in anticipating industrial production growth. Combining the information …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such … as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we … incorporate Google search data into a Bridge Equation Model, a version of which usually belongs to the suite of forecasting models …

limited to short-term predictions, the value of Google data for forecasting purposes is episodic, and the improvements in … forecasting accuracy are only modest. The results, obtained by (pseudo) out-of-sample forecast comparison, are robust to a state …

In this report we document the ETLAnow project. ETLAnow is a model for forecasting with big data. At the moment, it …