Models for Dependent Time Series

Authored by leading researchers in multivariate time series, this book introduces time series models based on lagged regression, particularly vector autoregressive models. It covers recent developments, such as graphical modeling and generalized lag models, and extends them to the important case of irregularly sampled time series. The book presents many examples and applications taken from a range of disciplines, including econometrics, finance, marketing, biometrics, and environmetrics. Software and datasets are available for download online.

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.