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
Other Titles in:
Quantitative/Statistical Research
Quantitative/Statistical Research
July 1994 | 104 pages | SAGE Publications, Inc
Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests. In addition, it covers such topics as: joint stationarity; testing for cointegration; testing for causality; and model order and forecast accuracy. Related models explained include transfer function, vector autoregression and error correction models.
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