Multivariate Tests for Time Series Models

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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

Series:
Quantitative Applications in the Social Sciences

Other Titles in:
Quantitative/Statistical Research (General)

August 1994 | 104 pages | SAGE Publications, Inc

Description
Contents

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

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