"The project deals with methods that are applicable across different scientific disciplines and is therefore inherently multidisciplinary. Our focus, however, will be on economic applications, as this is our field of expertise. Economic time series are sampled at different frequencies. For example, financial markets data are available on a (intra-)daily basis, while many macroeconomic series are observed monthly or quarterly. The typical approach adopted in empirical research is to align the data at the lowest sampling frequency and run regressions with same-frequency data. This leads to loss of information. In addition, the availability of high frequency financial data represents an opportunity to update expectations about low frequency macroeconomic time series – something that is of key importance to policy decision makers. Our research pertains to methods that allow us to handle mixed frequency data in empirical research as well as provide tools for policy makers to use the steady flow of high frequency data to help guide their decision making process. The research is based on a framework, using so called MIDAS, meaning Mi(xed) Da(ta) S(ampling) regressions that involve data sampled at different frequencies. This research opens up many new areas, some of which have already been studied, while many others remain unexplored. The purpose of this research project is to study the many unexplored areas ranging from systemic risk monitoring to macroeconomic forecasting for the Euro area for the purpose of policy decision."