An introduction to multiple time series analysis.
An expository account of multiple time series analysis is presented. Modeling several related time series together makes it possible to ascertain dynamic leading, lagging, and feedback relationships among the series; to produce more efficient forecasts and, in some situations; to develop control schemes. Procedures for building linear vector autoregressive moving average models are sketched and illustrated. An extension to parallel vector time series in a hierarchical framework is given.
Tiao
G C
GC
Graduate School of Business, University of Chicago, IL 60637.
D006302:Health Services Research / Q000379:methods*; D016014:Linear Models; D016011:Normal Distribution; D012906:Smoke / Q000009:adverse effects; D016019:Survival Analysis; D013997:Time Factors
D012906:Smoke