The project focuses on developing new probabilistic forecasting methods with applications in econometrics and finance. New methods of describing uncertainty using dynamic combination of forecasts refer to the idea that at a given moment it may not be clear which model has the best forecasting properties. Additionally, it may evolve with changes in, e.g. economic conditions. This concept has important implications for analyses of forecast uncertainty. However, all modern applications of statistical methods aimed at analyzing uncertainty regarding the formation of future economic values ​​are technically advanced and demanding in terms of computational power.

The project focuses on the problem of developing an optimal methodology (made available to practitioners) that can be used in macroeconomic forecasting (focusing on the description of prediction uncertainty) and in risk analyses conducted both in macroeconomics and finance. A better description of the uncertainty of macroeconomic forecasts is important from the point of view of those conducting economic policy and may also contribute to a better understanding of the role of uncertainty in economic analyses. It may also contribute to the development of better methods of forecasting changes in the economic situation. In addition, improved risk assessment methods are important in financial stability analyses – they can also contribute to the development of procedures and legal regulations contributing to the stability of the banking system and the entire financial system. The importance of this type of issues increases with the tightening of international economic connections. The practical importance of these issues is very significant (especially considering the recent financial crisis).