Models for dependent time series. (2015)

• Record Type: Book
• Further Information: Note: Granville Tunnicliffe Wilson, Marco Reale, John Haywood.
• Authors: Tunnicliffe-Wilson, Granville; Reale, Marco; (Mathematics professor), Haywood, John
• Contents: Introduction and overview; Examples of time series; Dependence within and between time series; Some of the challenges of time series modeling; Feedback and cycles; Challenges of high frequency sampling; Causal modeling and structure; Some practical considerations Lagged regression and autoregressive models ; Stationary discrete time series and correlation; Autoregressive approximation of time series; Multi-step autoregressive model prediction; Examples of autoregressive model approximation; The multivariate autoregressive model; Autoregressions for high lead time prediction; Model impulse response functions; The covariances of the VAR model; Partial correlations of the VAR model; Inverse covariance of the VAR model; Autoregressive Moving Average models; State space representation of VAR models; Projection using the covariance matrix; Lagged response functions of the VAR model Spectral analysis of dependent series ; Harmonic components of time series; Cycles and lags; Cycles and stationarity; The spectrum and cross-spectra of time series; Dependence between harmonic components; Bivariate and multivariate spectral properties; Estimation of spectral properties; Sample covariances and smoothed spectrum; Tapering and pre-whitening; Practical examples of spectral analysis; Harmonic contrasts in large samples The estimation of vector autoregressions ; Methods of estimation; The spectrum of a VAR model; Yule–Walker estimation of the VAR(p ) model; Estimation of the VAR(p ) by lagged regression; Maximum likelihood estimation, MLE; VAR models with exogenous variables, VARX; The Whittle likelihood of a time series model Graphical modeling of structural VARs ; The structural VAR, SVAR The directed acyclic graph, DAG; The conditional independence graph, CIG; Interpretation of CIGs; Properties of CIGs; Estimation and selection of DAGs; Building a structural VAR, SVAR; Properties of partial correlation graphs; Simultaneous equation modeling; An SVAR model for the Pig market: the innovations; A full SVAR model of the Pig market series VZAR: an extension of the VAR model ; Discounting the past; The generalized shift operator; The VZAR model; Properties of the VZAR model; Approximating a process by the VZAR model; Yule–Walker fitting of the VZAR; Regression fitting of the VZAR; Maximum likelihood fitting of the VZAR; VZAR model assessment Continuous time VZAR models ; Continuous time series; Continuous time autoregression and the CAR(1); The CAR(p ) model; The continuous time generalized shift The continuous time VZAR model, VCZAR Properties of the VCZAR model; Approximating a continuous process by a VCZAR; Yule–Walker fitting of the VCZAR model; Regression and ML estimation of the VCZAR Irregularly sampled series ; Modeling of irregularly sampled series; The likelihood from irregularly sampled data; Irregularly sampled univariate series models; The spectrum of irregularly sampled series; Recommendations on VCZAR model selection; A model of regularly sampled bivariate series; A model of irregularly sampled bivariate series Linking graphical, spectral and VZAR methods ; Outline of topics; Partial coherency graphs; Spectral estimation of causal responses; The structural VZAR, SVZAR; Further possible developments Bibliography Subject Index; Author Index
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.55015118; Time-series analysis -- Mathematical models
• Languages: English
• ISBNs: 9781420011500
• Related ISBNs: 9781584886501
• Notes: Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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