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200 10$aModels for dependent time series /$fGranville Tunnicliffe-Wilson, Department of Mathematics and Statistics, Lancaster University, UK; Marco Reale, School of Mathematics and Statistics, University of Canterbury, New Zealand; John Haywood, School of Mathematics and Statistics, Victoria University of Wellington, New Zealand
205   $a1st ed.
210  1$aBoca Raton :$cCRC Press,$d2015.
215   $a1 online resource (320 p.)
225 1 $aMonographs on Statistics and Applied Probability ;$vVolume 142
300   $aA Chapman & Hall book.
311 08$a9780367570521 
311 08$a0367570521 
311 08$a9781584886501 
311 08$a1584886501 
320   $aIncludes bibliographical references.
327   $a""Cover""; ""Contents""; ""Preface""; ""Chapter 1: Introduction and overview""; ""Chapter 2: Lagged regression and autoregressive models""; ""Chapter 3: Spectral analysis of dependent series""; ""Chapter 4: Estimation of vector autoregressions""; ""Chapter 5: Graphical modeling of structural VARs""; ""Chapter 6: VZAR: An extension of the VAR model""; ""Chapter 7: Continuous time VZAR models""; ""Chapter 8: Irregularly sampled series""; ""Chapter 9: Linking graphical, spectral and VZAR methods""; ""References""
330   $aModels for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater
410  0$aMonographs on statistics and applied probability (Series) ;$vVolume 142.
606   $aTime-series analysis
606   $aAutoregression (Statistics)
606   $aMathematical statistics
615  0$aTime-series analysis.
615  0$aAutoregression (Statistics)
615  0$aMathematical statistics.
676   $a519.5/5
676   $a519.55
700   $aTunnicliffe-Wilson$b Granville$01222049
702   $aReale$b Marco
702   $aHaywood$b John$c(Mathematics professor),
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996   $aModels for dependent time series$94327481
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