Basic Concepts (Bas)

Overview: This module covers the basic concepts needed to understand the uses and mechanics of seasonal adjustment. It can be taught with other modules that involve computer work, or it can be taught as a separate module with no computer work. If taught alone (the Introduction to Seasonal Adjustment Course), the module is usually expanded to 2 days to allow for more discussion of the policies and issues surrounding seasonal adjustment.

Prerequisites: None

Outline: The course examines the following topics:

• Basic definitions: time series, seasonal adjustment, trend-cycle, trading day, moving holidays, and benchmarking
• The general mechanics of seasonal adjustment, such as various types of filters used and multiplicative versus additive adjustment
• Overview and demonstration of X-12-ARIMA and SEATS, including review of input and output files for each program
• An overview of concepts and the notation of regARIMA modeling, including the basic regressors used for seasonal adjustment
• A review of the diagnostics available, including spectral and stability diagnostics
• A discussion of various issues surrounding seasonal adjustment, including
• Issues with production, including publishing trend-cycles
• Direct versus indirect adjustment of aggregate series
• Possible sources of revisions or changes to the seasonal factors, including a discussion of outliers and extreme values
• Frequency of data collection and issues involved with time consistency and benchmarking
• Other policy issues related to seasonal adjustment

Courses that contain this module:

Running X-12-ARIMA (RX12)

Overview: This module covers all the commands of X-12-ARIMA, and includes instruction on input files, running the program in Windows, reading the output, and assessing the results. The module is practical in nature, with some brief discussion of the theory behind the calculations.

Prerequisites: The "Introduction to Seasonal Adjustment" course.

Outline: The course examines the following topics:

• 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 the seasonal adjustment diagnostics, including spectral graphs and stability diagnostics
• A review of RegARIMA models and the various options and diagnostics available in X-12-ARIMA
• Computer work involving a wide range of sample series

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.

Courses that contain this module:

Running TRAMO/SEATS (RTS)

Overview: This module covers all the commands of TRAMO/SEATS, and includes instruction on input files, running the program in Windows, reading the output, and assessing the results. The module is practical in nature.

Prerequisites: The "Introduction to Seasonal Adjustment" course.

Outline: The course examines the following topics:

• 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
• A review of regARIMA models and the various options available, including the use of TERROR
• A review of the modeling and seasonal adjustment diagnostics available
• Computer work involving a wide range of sample series

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.

Courses that contain this module:

Advanced Diagnostics: Case Studies (AD)

Overview: This module uses examples and exercises to give the participants a detailed look at the diagnostics for seasonal adjustment and regARIMA modeling. Since there are more diagnostics available in X-12-ARIMA than in other seasonal adjustment programs, the course focuses on X-12-ARIMA. The module is both practical and technical, and we also discuss some of the theory behind the diagnostics.

Prerequisites: The "Running X-12-ARIMA" course or similar work experience is useful. We assume that participants are already familiar with the basics of seasonal adjustment. Topics require some knowledge of statistics, and some theoretical topics are covered.

Outline: The course examines the following topics:

• General graphical diagnostics
• Spectral diagnostics
• RegARIMA overview, tools, and diagnostics
• Seasonal adjustment stability diagnostics
• Other seasonal adjustment diagnostics
• Putting the diagnostics to work to improve the adjustment
• Diagnostics for Composite Series
• 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

Note: 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.

Courses that contain this module:

Advanced ARIMA Modeling (AAM)

Overview: ARIMA models are mathematical models of the autocorrelation in a time series. This module 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, we will discuss only univariate time series. The module is both practical and theoretical. The module is best when taught with in-class computer work (using either TRAMO or X-12-ARIMA), but could also be taught as lectures only.

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

Outline: The course examines the following topics:

• Overview of regARIMA modeling
• Models for time series
• Model identification
• Fitting ARIMA Models
• Regression models with ARIMA errors (regARIMA models)
• Forecasting with regARIMA models
• Spectral methods
• Linear filters for seasonal adjustment

Courses that contain this module: