Catherine Hood Consulting

Advanced ARIMA Modeling for Forecasting

and Seasonal Adjustment

ARIMA models are mathematical models of the autocorrelation in a time series. They are used for forecasting time series in many different fields. This course is designed for users of seasonal adjustment and forecasting software who would like a deeper understanding of ARIMA models and the Box-Jenkins method. Because the focus is on forecasting for seasonal adjustment and ARIMA-model-based adjustment as with SEATS, we will discuss only univariate time series. The course is both practical and theoretical. The course is best when taught with in-class computer work (using either TRAMO or X-12-ARIMA), but could also be taught as lectures only.

Duration: 3 days

Target Audience:
This course is intended for persons with a background in econometrics or statistics who are interested in learning more about the details of ARIMA modeling and the diagnostics involved. Since ARIMA modeling is an important part of using SEATS, this course is especially useful to TRAMO/SEATS users. The course is limited to 10 persons.

None, but topics require some knowledge of statistics, for example, the participants should understand terms like mean, normal distribution, covariance, and linear regression.

Topics Covered:
The course examines the following topics:

  • ARMA processes
  • Box-Jenkins method for ARIMA modeling
  • Models for time series
  • Model identification
  • Fitting ARIMA models
  • Estimation methods
  • Regression models with ARIMA errors (regARIMA models)
    • Detecting and removing outliers
    • Detecting trading day and moving holiday effects
  • Forecasting methods and evaluating forecasting performance
  • Spectral methods
  • Linear filters for seasonal adjustment

The course will involve the practical application of concepts through the use of case studies, group discussion, and exercises.

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Last modified: 1 Apr 2013