This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for periodic integration and in forecasting is provided. The second part discusses multivariate periodic autoregressive models. It provides an overview of periodic cointegration models, as these are the most relevant. This overview contains single-equation type tests and a full-system approach based on generalized method of moments. All methods are illustrated with extensive examples, and the book will be of interest to advanced graduate students and researchers in econometrics, as well as practitioners looking for an understanding of how to approach seasonal data. Provides an up-to-date survey of periodic time series models for seasonal data. Investigates such areas as seasonal time series; periodic time series models; periodic integration; and periodic cointegration. Contains many contemporary empirical examples and results.

Additional Metadata
Keywords mathematical statistics, time series models
Publisher Oxford University Press
ISBN 978-0-19-924202-3
Persistent URL hdl.handle.net/1765/2036
Citation
Franses, Ph.H.B.F, & Paap, R. (2004). Periodic Time Series Models. Oxford University Press. Retrieved from http://hdl.handle.net/1765/2036