03184ntm a22005057i 4500 000128935 CZ-PrVSE 20210905195618.0 m d cr n|||||||||| 210905s2021 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Hron, Filip ISIS:125821 dis Redukce dimenzionality v makroekometrii eng Dimensionality reduction in macroeconometrics models / Filip Hron 2021 ?? stran : digital, PDF soubor Vedoucí práce: Lukáš Frýd Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2021 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2021 Standard vector autoregressive approach is highly used in the macroeconometrics. However, the problem with degrees of freedom arises. It is usually quite challenging to include a lot of information in the model. Subsequently, the predictive power declines, and the structural analysis may be misleading. In general, even though there is plenty of available macroeconomic series, we can operate with only an insufficient portion. More generally, the thesis contains three advanced methodology guidelines included Bayesian, machine learning, and econometric approaches. These approaches deal with dimensional reduction according to the two different methodology procedures. The first procedure reduces the predictors' space with a few common unobserved factors. On the contrary, the second one is more focused on shrink the parameter space based on the particular criteria. In the thesis, we empirically examine the Czech main macroeconomic indicators with a main focus on the unemployment rate. We found that the advanced approaches produce effective unemployment rate forecasts compared to the standard ARMA or VAR models. Moreover, we capture the individual impacts of the indicators on the unemployment rate using the structural VAR analysis. Způsob přístupu: Internet ekonometrie a operační výzkum [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas forecasting time series unemployment rate vector autoregression dimensional reduction Frýd, Lukáš ISIS:78722 ths Formánek, Tomáš ISIS:46723 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/75329/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/75329 Hlavní práce https://insis.vse.cz/zp/75329/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/75329/posudek/oponent/71911 Oponentura https://insis.vse.cz/zp/75329/podrobnosti dc:identifier NEPOSILAT VSKP vse75329 210831 75329