Catherine Hood Consulting

Advanced Seasonal Adjustment


This course is a combination of three courses: Advanced Seasonal Adjustment with SEATS, Advanced Diagnostics: Case Studies, and ARIMA Modeling for Forecasting and Seasonal Adjustment.

This course is a good follow-up to the Seasonal Adjustment with X-13ARIMA-SEATS in Windows® course.

Duration: 5-7 days

Target audience:
Persons with a background in econometrics or statistics who are interested in learning more about the details of X-13ARIMA-SEATS, seasonal adjustment diagnostics, and ARIMA modeling. The course is limited to 10 persons.

The "Running X-13ARIMA-SEATS" course or similar work experience in X-12-ARIMA or X-13ARIMA-SEATS is useful. We assume that participants are already familiar with the basics of seasonal adjustment. Topics require some knowledge of statistics and/or econometrics, and we cover some theoretical topics.

Topics Covered:
The course examines the topics covered in the Advanced Seasonal Adjustment with SEATS, Advanced Diagnostics: Case Studies, and ARIMA Modeling for Forecasting and Seasonal Adjustment.

  • Review of the specification options available in X-13ARIMA-SEATS
  • Review of the diagnostics available in X-13ARIMA-SEATS, including theoretical background when appropriate
    • General graphical diagnostics
    • Spectral diagnostics
    • RegARIMA overview, tools, and diagnostics
    • Seasonal adjustment stability diagnostics
    • Other seasonal adjustment diagnostics
    • Diagnostics for composite series
  • Putting the diagnostics to work to improve the adjustment, including (but not limited to)
    • How to decide if a series is seasonal and/or adjustable
    • Strategies for dealing with residual spectral peaks
    • Strategies for deciding on ARIMA models and regression variables
    • Issues involving shortening the series or shortening the series for regARIMA modeling
    • Adjusting series that are a combination of smaller series (direct versus indirect adjustment of composite series)
    • Adjusting series with different variability in different months (or quarters)
  • 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 and details of the SEATS algorithm
  • Demonstration, looking at possible model and adjustment options for one series starting with only the data.
  • Computer work involving a wide range of sample series

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

Note: If time permits at the end of the course, participants will have the chance to work on sample series provided or on their own series. Participants are encouraged to bring sample time series with them to class as either text files or in Excel format.

Trademarks and Copyrights

  • TRAMO/SEATS is written by Victor Gomez and Agustin Maravall and is available free of charge from the Bank of Spain web site.
  • X-12-ARIMA and X-13ARIMA-SEATS are products of the U.S. Census Bureau and are available free of charge.
  • Windows Operating System, Microsoft® Windows, Copyright Microsoft Corporation

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Last modified: 20 Mar 2021

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