Multivariate Tests for Time Series Models
Jeffrey B. Cromwell
- Design of Harmony (Chandler, AZ)
Walter C. Labys
- West Virginia University, USA
Michael J. Hannan
- Edinboro University, Pennsylvania
Michel Terraza
- Montpellier University
Purchase
Paperback
ISBN:
9780803954403
Available from
January 0001
Description
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Contents
Introduction
Introduction
Testing for Joint Stationarity, Normality and Independence
Testing for Joint Stationarity, Normality and Independence
Testing for Cointegration
Testing for Cointegration
Testing for Causality
Testing for Causality
Multivariate Linear Model Specification
Multivariate Linear Model Specification
Multivariate Nonlinear Specification
Multivariate Nonlinear Specification
Model Order and Forecast Accuracy
Model Order and Forecast Accuracy
Computational Methods for Performing the Tests
Computational Methods for Performing the Tests
Description
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Contents
Introduction
Introduction
Testing for Joint Stationarity, Normality and Independence
Testing for Joint Stationarity, Normality and Independence
Testing for Cointegration
Testing for Cointegration
Testing for Causality
Testing for Causality
Multivariate Linear Model Specification
Multivariate Linear Model Specification
Multivariate Nonlinear Specification
Multivariate Nonlinear Specification
Model Order and Forecast Accuracy
Model Order and Forecast Accuracy
Computational Methods for Performing the Tests
Computational Methods for Performing the Tests
Multivariate Tests for Time Series Models
July 1994 | 104 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|---|---|---|
| Paperback | 28/02/2026 | 9780803954403 | $55.00 |
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Table Of Contents:
- 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