03210ntm a22005177i 4500 000701131 CZ-PrVSE 20230827080715.0 m d cr n|||||||||| 230827s2023 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Daş, Berk ISIS:158045 dis Nowcasting with Machine Learning Methods eng Nowcasting with Machine Learning Methods / Berk Daş 2023 ?? stran : digital, PDF soubor Vedoucí práce: Miroslav Plašil Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2023 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2023 This master's thesis aims to nowcast the Czech GDP growth and compare several machine learning regression models' performances. While GDP is the most important macroeconomic indicator, there is a publication lag of around 70 days in the Czech Republic, and it is published quarterly. This publication lag hinders rapid decision-making and effective policy implementation. For this reason, while the early estimation of GDP is crucial, many government agencies and Central Banks are working and using models to nowcast GDP. There are many monthly independent variables in GDP nowcasting models. The MIDAS method suggested in the literature was used for the variables explained at different frequencies to work in a regression model in the most effective way. Machine learning regression models were run after the MIDAS application, estimates were created for train and test sets, and errors were obtained. Performance improvements to machine learning models have been realized with hyperparameter tuning. While this means finding the optimal values of the specific parameters for all models, the amount of error is noticeably reduced in the results obtained from the train and the test set. This thesis compared the performances of all machine learning models used on tables and figures. Způsob přístupu: Internet official statistics [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Nowcasting Gross Domestic Product Czech Republic MIDAS Machine Learning Plašil, Miroslav ola2012709459 ths Karel, Tomáš vse2015858033 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/80340/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/80340 Hlavní práce https://insis.vse.cz/zp/80340/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/80340/posudek/oponent/79932 Oponentura https://insis.vse.cz/zp/80340/podrobnosti dc:identifier NEPOSILAT VSKP vse80340 230824 80340