Periodic Time Series Models by Philip Hans FransesPeriodic Time Series Models by Philip Hans Franses

Periodic Time Series Models

byPhilip Hans Franses, Richard Paap

Paperback | April 6, 2004

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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 alarge 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 inforecasting 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.
Philip Hans Franses is Professor of Applied Econometrics and Professor of Marketing Research at Erasmus University, Rotterdam. He is the author of a number of books, including Periodicity and Stochastic Trends in Economic Time Series (OUP, 1996). Richard Paap is a Postdoctoral Researcher at the Econometric Institute in Erasmus Univers...
Title:Periodic Time Series ModelsFormat:PaperbackDimensions:164 pages, 9.21 × 6.14 × 0.39 inPublished:April 6, 2004Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199242038

ISBN - 13:9780199242030

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Table of Contents

1. Introduction2. Properties of Seasonal Time Series3. Univariate Periodic Time Series Models4. Periodic Models for Trending Data5. Multivariate Periodic Time Series ModelsAppendix