Multivariate tests for time series models / Jeff B. Cromwell ... [and others].

Format:

Book

Contributor:

Cromwell, Jeff B.

Series:

Quantitative applications in the social sciences ; no. 07-100.

A Sage university papers series. Quantitative applications in the social sciences ; no. 07-100

Language:

English

Subjects (All):

Time-series analysis.

Social sciences--Statistical methods.

Social sciences.

Physical Description:

vi, 98 pages : illustrations ; 22 cm.

Place of Publication:

Thousand Oaks, Calif. : Sage Publications, [1994]

Summary:

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.

Contents:

Relations Between Variables 3

Joint Stationarity 3

Covariance and Correlation 5

Time Series Tests and Model Building 6

2. Testing for Joint Stationarity, Normality, and Independence 9

Testing for Joint Stationarity 9

Fountis-Dickey Test 10

Transformations 11

Testing for Normality 12

Skewness and Kurtosis Test 13

Testing for Independence 14

Portmanteau Test 15

Pierce-Haugh Test 15

3. Testing for Cointegration 17

Cointegrating Regression Durbin-Watson (CRDW) Test 19

Dickey-Fuller (DF) Test 21

Augmented Dickey-Fuller (ADF) Test 23

Engle-Granger Tests 25

Johansen Test 29

Granger-Lee Test 30

4. Testing for Causality 32

Granger Causality Test 33

Sims Test 40

Geweke-Meese-Dent Test 43

Pierce-Haugh Test 46

Geweke Test 49

5. Multivariate Linear Model Specification 56

Transfer Function Models (TF) 56

Vector Autoregressive Models (VAR) 58

Vector Moving Average Models (VMA) 59

Testing Decompositions and Impulse Functions 62

Bayesian Vector Autoregressive Models (BVAR) 65

Vector Autoregressive Moving Average Models (VARMA) 67

Error Correction Models (ECM) 68

State-Space Models 70

6. Multivariate Nonlinear Models 71

Feedforward Neural Networks 72

7. Model Order and Forecast Accuracy 73

Testing for Model Order 74

Testing for Forecast Accuracy 76

Accuracy of Individual Models 78

Nonparametric Tests 80

Comparative Accuracy Across Models 82

8. Computational Methods for Performing the Tests 84

Appendix Statistical Tables 87

A.1 Critical Values for the Dickey-Fuller Test 87

A.2 Critical Values and Power of the Engle-Granger Test 88

A.3 Critical Values for the Engle-Yoo Cointegration Test 90.

Notes:

Includes bibliographical references.

ISBN:

0803954409

OCLC:

30078968