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Multivariate Tests for Time Series Models
- Jeff B. Cromwell - Design of Harmony (Chandler, AZ), West Virginia University, USA
- Walter C. Labys - West Virginia University, USA
- Michael J. Hannan - Edinboro University, Pennsylvania
- Michel Terraza - Montpellier University
Volume:
100
July 1994 | 104 pages | SAGE Publications, Inc
Which time series test should a researcher chose to best describe the interactions among a set of time series variables? Aimed at providing social scientists with practical guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests. Other topics it covers are joint stationarity, testing for cointegration, testing for Granger causality, and testing for model order, and forecast accuracy. Related models explained include transfer function, vector autoregression, error correction models, and others. Readers with a working knowledge of time series regression will find this helpful book accessible.
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Introduction
Testing for Joint Stationarity, Normality and Independence
Testing for Cointegration
Testing for Causality
Multivariate Linear Model Specification
Multivariate Nonlinear Specification
Model Order and Forecast Accuracy
Computational Methods for Performing the Tests
