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

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Advanced Seasonal Adjustment with TRAMO/SEATS

Purpose:
This course is a combination of the Seasonal Adjustment with TRAMO/SEATS for Windows® course and the ARIMA Modeling for Forecasting and Seasonal Adjustment course.

Duration: 5 days

Target audience:
Persons with a background in econometrics or statistics who are interested in learning more about the details of TRAMO/SEATS and ARIMA modeling. 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 Seasonal Adjustment with TRAMO/SEATS for Windows® and ARIMA Modeling for Forecasting and Seasonal Adjustment.

• 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 input parameters available in TRAMO/SEATS
• Running TRAMO/SEATS in automatic mode and in batch mode
• 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
• Using TERROR
• 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.

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Last update: 10 January 2007