Models for Dependent Time Series

ISBN-13: 9780367570521 / Angielski / Miękka / 2020 / 340 str.

Granville Tunnicliffe Wilson; Marco Reale; John Haywood

Opis

This book addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. It shows how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The book presents several extensions to the standard autoregressive mo

Spis treści

Introduction and Overview. Lagged Regression and Autoregressive Models. Spectral Analysis of Dependent Series. The Estimation of Vector Autoregressions. Graphical Modeling of Structural VARs. VZAR: An Extension of the VAR Model. Continuous Time VZAR Models. Irregularly Sampled Series. Linking Graphical, Spectral and VZAR Methods. Bibliography. Index.

O autorze

Granville Tunnicliffe Wilson is a reader emeritus in the Department of Mathematics and Statistics at Lancaster University, UK. His research focuses on methodology and software for time series modeling and prediction.

Marco Reale is an associate professor in the School of Mathematics and Statistics at the University of Canterbury, New Zealand. His research interests include time series analysis, statistical learning, and stochastic optimization.

John Haywood is a senior lecturer in the School of Mathematics and Statistics at Victoria University of Wellington, New Zealand. His research interests include time series analysis, seasonal modeling, and statistical applications, particularly in ecology.