Multiple Time Series Models
Book Details
Format
Paperback / Softback
Book Series
Quantitative Applications in the Social Sciences
ISBN-10
1412906563
ISBN-13
9781412906562
Publisher
SAGE Publications Inc
Imprint
SAGE Publications Inc
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Nov 2nd, 2006
Print length
120 Pages
Weight
156 grams
Dimensions
21.40 x 13.90 x 0.70 cms
Ksh 7,400.00
Manufactured on Demand
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Reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. This book focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. It also reviews arguments for and against using multi-equation time series models.
Many analyses of time series data involve multiple, related variables. Multiple Time Series Models presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.
Key Features
Key Features
- Offers a detailed comparison of different time series methods and approaches.
- Includes a self-contained introduction to vector autoregression modeling.
- Situates multiple time series modeling as a natural extension of commonly taught statistical models.
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