Forecasting is the process of making predictions of the future based on past and present data, the aim of which is to reduce risk in decision-making. The key element in the prediction process is the environment that determines the set of possible states of nature: certainty, uncertainty and risk. Assessing uncertainties related to future macroeconomic development is a long established element of monetary and fiscal policy and indeed of most micro- and macroeconomic decisions. Without going into wide and well-covered review of the problem, the exposition is limited to stating that the uncertainty is one of the main elements of inflation and economic growths. There is a myriad of publications on macroeconomic forecasting based on econometric models (DSGE, VAR, etc.) in the scientific journals, however due to the low reliability of these models concerning the accuracy of forecasts of selected macroeconomic indicators, central banks began constructing the so-called predictions: Experts’ forecasts. Policy decisions in real-time are based on assessments of the recent past and current economic condition under a high degree of uncertainty. Short-term forecasts at central banks are typically formed by sector experts' views on economic developments in conjunction with formal models. In this process, forecasters have to take into account that statistics are released with a long delay, are subsequently revised and are available at different frequencies. In addition, the data generating process is typically unknown and likely to change over time. Therefore the accuracy level of macroeconomic expert forecasts becomes an issue of great importance. This paper will present the results of a study that aims to determine the impact of financial market volatility on experts’ ex-post forecasts errors. The main objective of the research is gaining new knowledge in the topic of the evaluation of interrelations in uncertainties which allow developing the methods of practical implementation of macroeconomic uncertainties in case of data being of different frequencies. For the purposes of this research, the following research hypothesis was formulated: applying Midas methodology lead to an improvement in accuracy of estimation of macroeconomic uncertainty measures in case of data of different frequencies.