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010   $a3-030-46347-8
024 7 $a10.1007/978-3-030-46347-2
035   $a(CKB)4100000011413952
035   $a(MiAaPQ)EBC6331636
035   $a(DE-He213)978-3-030-46347-2
035   $a(PPN)250216663
035   $a(EXLCZ)994100000011413952
100   $a20200831d2020      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aTime Series in Economics and Finance$b[electronic resource] /$fby Tomas Cipra
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (409 pages) $cillustrations
311   $a3-030-46346-X 
320   $aIncludes bibliographical references and index.
327   $a1. Introduction -- I. Subject of Time Series -- 2. Random Processes -- II. Decomposition of Economic Time Series -- 3. Trend -- 4. Seasonality and Periodicity -- 5. Residual Component -- III. Autocorrelation Methods for Univariate Time Series -- 6. Box-Jenkins Methodology -- 7. Autocorrelation Methods in Regression Models -- IV. Financial Time Series -- 8. Volatility of Financial Time Series -- 9. Other Methods for Financial Time Series -- 10. Models of Development of Financial Assets -- 11. Value at Risk -- V. Multivariate Time Series -- 12. Methods for Multivariate Time Series -- 13. Multivariate Volatility Modeling -- 14. State Space Models of Time Series -- References -- Index.
330   $aThis 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.
606   $aStatistics 
606   $aEconometrics
606   $aEconomics, Mathematical 
606   $aFinancial engineering
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
606   $aEconometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/W29010
606   $aQuantitative Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/M13062
606   $aFinancial Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/612020
615  0$aStatistics .
615  0$aEconometrics.
615  0$aEconomics, Mathematical .
615  0$aFinancial engineering.
615 14$aStatistics for Business, Management, Economics, Finance, Insurance.
615 24$aEconometrics.
615 24$aQuantitative Finance.
615 24$aFinancial Engineering.
676   $a330.015195
700   $aCipra$b Tomas$4aut$4http://id.loc.gov/vocabulary/relators/aut$01015116
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996418252203316
996   $aTime Series in Economics and Finance$92368793
997   $aUNISA