000			01905nam a22002177a 4500
005			20230311104905.0
008			230311b ||||| |||| 00| 0 eng d
020			_a9780367241018
040			_cAL
041			_aeng
082			_223 _a519.55015118 _bWILM
100			_aGranville Tunnicliffe Wilson _975036
245			_aModels for dependent time series _bMonographs on statistics and applied probability
260			_aNewYork _bCRC Press _c2019
300			_axv,323p. _bHB _c23x16cm.
365			_2General _a8197 _b₹2336.00 _c₹ _d₹2995.00 _e22% _f3-03-2023
520			_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
650			_aStatistics _975037
700			_aReale, Marco; Haywood John _975038
942			_2ddc _cBK
999			_c226997 _d226997