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https://elib.bsu.by/handle/123456789/233371
Полная запись метаданных
Поле DC | Значение | Язык |
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dc.contributor.author | Malugin, V. I. | |
dc.contributor.author | Novopoltsev, A. Yu. | |
dc.contributor.author | Hryn, N. V. | |
dc.date.accessioned | 2019-10-29T12:06:17Z | - |
dc.date.available | 2019-10-29T12:06:17Z | - |
dc.date.issued | 2019 | |
dc.identifier.citation | Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the Twelfth Intern. Conf., Minsk, Sept. 18-22, 2019. – Minsk : BSU, 2019. – P. 228-231. | |
dc.identifier.isbn | 978-985-566-811-5 | |
dc.identifier.uri | http://elib.bsu.by/handle/123456789/233371 | - |
dc.description.abstract | In the practice of a central bank, prudential indicators based on aggregated macroeconomic and financial data [1] are used to analyze financial stability of the banking system. There is an evidence that it's may be not enough to use aggregated macroeconomic and financial data to identify situations of growing systemic risks threatening economic and financial stability [2]. Obviously, the financial stability of the banking sector and the economy as a whole essentially depends on the financial state (creditworthiness) of certain categories of enterprises, as well as the level of credit risk for various sectors of the economy. For this reason, firm-level data is going to be more appealing for central banks. It's reported that, in contrast to aggregated economic and financial data, microdata allow for more flexible and differential analysis. This makes the statistical indicators of solvency and financial stability based on microdata a useful complementary tool for analysis of actual economic and financial processes | |
dc.language.iso | en | |
dc.publisher | Minsk : BSU | |
dc.subject | ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика | |
dc.subject | ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика | |
dc.title | Analysis of financial stability of the Belarusian economy based on microdata and expert information | |
dc.type | conference paper | |
Располагается в коллекциях: | 2019. Computer Data Analysis and Modeling : Stochastics and Data Science |
Полный текст документа:
Файл | Описание | Размер | Формат | |
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228-231.pdf | 223,71 kB | Adobe PDF | Открыть |
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