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.
Health Services Research Linear Models