Introduction to Multiple Time Series Analysis

Lutkepohl, H

ISBN 10: 3540531947 / ISBN 13: 9783540531944

Edité par Springer-Verlag, 1991

A propos de cet article

This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In fair condition, suitable as a study copy. No dust jacket. Re-bound by library. Foxing on page edges. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1150grams, ISBN:3540531947.

N° de réf. du vendeur 7060859

Détails bibliographiques

• Titre: Introduction to Multiple Time Series Analysis
• Éditeur: Springer-Verlag
• Date de parution: 1991
• Langue: anglais
• ISBN à 10 chiffres: 3540531947
• ISBN à 13 chiffres: 9783540531944
• Reliure: Couverture rigide
• État de l'article: Fair
• Catalogues du vendeur:
  • Economics

A propos de ce titre

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 as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.

Présentation de l'éditeur

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.