Our full list of time series and seasonal adjustment courses:

• Introduction to Seasonal Adjustment
• Seasonal Adjustment with X-12-ARIMA in Windows®
• Seasonal Adjustment with TRAMO/SEATS for Windows®
• Seasonal Adjustment with X-12-ARIMA and TRAMO/SEATS in Windows®
• Advanced Diagnostics: Case Studies
• Advanced ARIMA Modeling for Forecasting and Seasonal Adjustment
• Advanced Seasonal Adjustment with X-12-ARIMA
• Advanced Seasonal Adjustment with X-12-ARIMA and Advanced ARIMA Modeling
• Advanced Seasonal Adjustment with TRAMO/SEATS
• Advanced Seasonal Adjustment with X-12-ARIMA and TRAMO/SEATS

Back to the Courses Main page

Advanced Seasonal Adjustment with X-12-ARIMA

Purpose:
This course is a combination of the Seasonal Adjustment with X-12-ARIMA in Windows® course and the Advanced Diagnostics: Case Studies course.

Duration: 5 days

Target audience:
This course is intended for persons interested in learning the details of X-12-ARIMA and its diagnostics. The course is limited to 10 persons.

Prerequisites:
None, only general prior knowledge of time series is assumed. Some topics require some knowledge of statistics, for example, the participants should understand terms like mean, covariance, and linear regression.

Topics Covered:
The course examines the topics covered in the Seasonal Adjustment with X-12-ARIMA in Windows® course and the Advanced Diagnostics: Case Studies course.

• Basic definitions, including time series, seasonal adjustment, trend-cycle, trading day, moving holidays, and benchmarking
• The general syntax of input specification files
• All of the specification functions available in X-12-ARIMA
• Running X-12-ARIMA in both single-series and batch mode
• The basic X-11 algorithm, in detail
• A review of RegARIMA models
• Review of the diagnostics available in X-12-ARIMA, 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)
• 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.

Windows^® is a registered trademark of Microsoft Corporation

Last update: 10 January 2007