Introduction to Multiple Time Series Analysis

Lutkepohl, Helmut

ISBN 10: 3540569405 / ISBN 13: 9783540569404

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 1993

About this Item

Deals with analyzing and forecasting multiple time series. This textbook discusses models such as vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models, and methods like estimation, specification, and checking the adequacy of these models. Num Pages: 545 pages, biography. BIC Classification: KCA; KCH. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 244 x 170 x 29. Weight in Grams: 940. . 1993. 2nd ed. 1993. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9783540569404

Bibliographic Details

• Title: Introduction to Multiple Time Series Analysis
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Publication Date: 1993
• Language: English
• ISBN 10: 3540569405
• ISBN 13: 9783540569404
• Binding: Soft cover
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

This graduate level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.