01829nam a22001937a 450000500170000000800410001702000180005804000070007604100080008308200270009110000330011824500870015126000290023830000270026736500630029452012320035765000150158970000310160420230311104905.0230311b           ||||| |||| 00| 0 eng d  a9780367241018  cAL  aeng  223a519.55015118bWILM  aGranville Tunnicliffe Wilson  aModels for dependent time seriesbMonographs on statistics and applied probability  aNewYorkbCRC Pressc2019  axv,323p.bHBc23x16cm.  2Generala8197b₹2336.00c₹d₹2995.00e22%f3-03-2023  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 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 material for the remaining chapters, which cover the construction of structural models and the extension of vector autoregressive modeling to high frequency, continuously recorded, and irregularly sampled series. The final chapter combines these approaches with spectral methods for identifying causal dependence between time series. Web Resource A supplementary website provides the data sets used in the examples as well as documented MATLAB® functions and other code for analyzing the examples and producing the illustrations. The site also offers technical details on the estimation theory and methods and the implementation of the models  aStatistics  aReale, Marco; Haywood John