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Time series in economics and finance
This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series...
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Lenguaje: | eng |
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Springer
2020
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Acceso en línea: | https://dx.doi.org/10.1007/978-3-030-46347-2 http://cds.cern.ch/record/2729492 |
_version_ | 1780966411915493376 |
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author | Cipra, Tomas |
author_facet | Cipra, Tomas |
author_sort | Cipra, Tomas |
collection | CERN |
description | This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series methods, such as cointegration and recursive state space modeling. It also includes numerous practical examples to demonstrate the theory using real-world data, as well as exercises at the end of each chapter to aid understanding. This book serves as a reference text for researchers, students and practitioners interested in time series, and can also be used for university courses on econometrics or computational finance. |
id | cern-2729492 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
publisher | Springer |
record_format | invenio |
spelling | cern-27294922021-04-21T18:05:08Zdoi:10.1007/978-3-030-46347-2http://cds.cern.ch/record/2729492engCipra, TomasTime series in economics and financeMathematical Physics and MathematicsThis book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series methods, such as cointegration and recursive state space modeling. It also includes numerous practical examples to demonstrate the theory using real-world data, as well as exercises at the end of each chapter to aid understanding. This book serves as a reference text for researchers, students and practitioners interested in time series, and can also be used for university courses on econometrics or computational finance.Springeroai:cds.cern.ch:27294922020 |
spellingShingle | Mathematical Physics and Mathematics Cipra, Tomas Time series in economics and finance |
title | Time series in economics and finance |
title_full | Time series in economics and finance |
title_fullStr | Time series in economics and finance |
title_full_unstemmed | Time series in economics and finance |
title_short | Time series in economics and finance |
title_sort | time series in economics and finance |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1007/978-3-030-46347-2 http://cds.cern.ch/record/2729492 |
work_keys_str_mv | AT cipratomas timeseriesineconomicsandfinance |