Modern (Multiple) Time Series Models: The Dynamic System

2017 ◽  
pp. 115-148 ◽  
Author(s):  
Koen H. Pauwels
Keyword(s):  
Time Series ◽  
Dynamic System ◽  
Time Series Models ◽  
Multiple Time ◽  
Multiple Time Series

Analyzing Permanent and Transient Influences in Multiple Time Series Models

1983 ◽  
Vol 1 (1) ◽  
pp. 57 ◽  
Author(s):  
Joseph A. Machak ◽  
W. Allen Spivey ◽  
William J. Wrobleski
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Multiple Time ◽  
Multiple Time Series

Analyzing Permanent and Transient Influences in Multiple Time Series Models

1983 ◽  
Vol 1 (1) ◽  
pp. 57-65
Author(s):  
Joseph A. Machak ◽  
W. Allen Spivey ◽  
William J. Wrobleski
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Multiple Time ◽  
Multiple Time Series

scholarly journals Diagnostic Tests for Multiple Time Series Models

1982 ◽  
Vol 10 (1) ◽  
pp. 114-120 ◽  
Author(s):  
D. S. Poskitt ◽  
A. R. Tremayne
Keyword(s):  
Time Series ◽  
Diagnostic Tests ◽  
Time Series Models ◽  
Multiple Time ◽  
Multiple Time Series

Forecasting with Diagonal Multiple Time Series Models: An Extension of Univariate Models

1983 ◽  
Vol 20 (1) ◽  
pp. 58-63 ◽  
Author(s):  
Sushila Umashankar ◽  
Johannes Ledolter
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Parameter Estimates ◽  
Time Series Models ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Univariate Time Series ◽  
Market Situation ◽  
Random Shocks ◽  
Special Case

The authors discuss a special case of multiple time series models in which the autoregressive and moving average parameter matrices are diagonal. These models, called MTS-D, are appropriate if the random shocks which drive the series are only contemporaneously correlated. The flexibility and usefulness of this approach are illustrated in the context of a competitive market situation with five sales series, where individual components follow different univariate time series models. Theoretical as well as empirical justifications for expecting more precise parameter estimates are given. Forecasts from the model compare favorably with those from the univariate models.

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