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An introduction to multiple time series analysis

G C Tiao1

  • 1Graduate School of Business, University of Chicago, IL 60637.

Medical Care
|May 1, 1993
PubMed
Summary

This study introduces multiple time series analysis for understanding dynamic relationships and improving forecasts. It details methods for vector autoregressive moving average models and their extensions.

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Area of Science:

  • Statistics
  • Econometrics
  • Time Series Analysis

Background:

  • Analyzing individual time series can miss complex interdependencies.
  • Understanding dynamic relationships is crucial for accurate forecasting and control.

Purpose of the Study:

  • To present an expository account of multiple time series analysis.
  • To illustrate the benefits of modeling related time series jointly.
  • To outline procedures for building and extending vector autoregressive moving average (VARMA) models.

Main Methods:

  • Modeling multiple related time series concurrently.
  • Utilizing linear vector autoregressive moving average (VARMA) models.
  • Extending methodologies to parallel vector time series within a hierarchical framework.

Main Results:

  • Enables ascertainment of leading, lagging, and feedback dynamics among series.
  • Facilitates more efficient forecasting compared to univariate methods.
  • Provides a foundation for developing control schemes for multivariate systems.

Conclusions:

  • Joint modeling of time series offers significant advantages in understanding complex systems.
  • VARMA models and their hierarchical extensions are powerful tools for multivariate time series analysis.
  • This approach enhances predictive accuracy and aids in system control.

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