1.     What is X-12-ARIMA?

X-12-ARIMA is a seasonal adjustment program developed at the U.S. Census Bureau. The program is based on the Bureau's earlier X-11 program and the X-11-ARIMA program developed at Statistics Canada.

Improvements in X-12-ARIMA as compared to X-11 are:

• Use of regARIMA models to
  • forecast the series, allowing us to use better, symmetric moving average filters that give us generally smaller revisions to the seasonal factors and
  • prior-adjust for outliers, TD, and holidays
• New diagnostic tools
• Wider variety of moving average options
• A new input syntax and new output files

2.     How do I get a copy of X-12-ARIMA?

3.     How do I run X-12-ARIMA?

Before running X-12-ARIMA, you need to write an input specification file. The input specification file is a text file used to specify various program options. The file must end with a file extension “.spc”. An example input specification file:

series{
  period = 12 
  title = 'Example for the FAQ'
  file = 'test.dat' 
  format = 'datevalue' 
  savelog = peaks  
} 
transform{function=log} 
arima{model = (0 1 1)(0 1 1)}
regression{variables =(td)}
outlier{ } 
check{ }
forecast{maxlead = 24}
X11{ }
slidingspans{ }
history{estimates=(fcst sadj sadjchng)}

X-12-ARIMA is a DOS program, but there is a Windows version available so that you don't have to go to a DOS prompt. The Windows interface will also write the input specifications files for you.

4.     How do I find what I need from the output file?

Usually X-12-ARIMA generates many files in a single run. Some of the files it can generate:

• output file
• error file
• log file
• diagnostic files
• graphics output files

• The seasonally adjusted series is in Table D 11.
• The trend-cycle is in Table D 12.
• The combined (seasonal/trading day/holiday) factors are in Table D 16.
• The seasonal factors alone are in Table D 10.

5.     How does X-12-ARIMA estimate the trend and seasonal components?

6.     How does X-12-ARIMA estimate trading day and moving holiday components?

7.     What seasonal filters are available in X-12-ARIMA?

The seasonal filters available in X-12-ARIMA consist of weighted averages of consecutive values within a given month or quarter. An n x m moving average is an m-term simple average taken over n consecutive sequential spans.

An example of a 3x3 filter for January 1999 (or Quarter 1, 1991) is:

1997.1 + 1998.1 + 1999.1 +
         1998.1 + 1999.1 + 2000.1 +
                  1999.1 + 2000.1 + 2001.1
___________________________________________
                    9

An example of a 3x5 filter for January 1999 (or Quarter 1, 1999) is:

1996.1 + 1997.1 + 1998.1 + 1999.1 + 2000.1 + 
         1997.1 + 1998.1 + 1999.1 + 2000.1 + 2001.1 + 
                  1998.1 + 1999.1 + 2000.1 + 2001.1 + 2002.1 
_____________________________________________________________
                             15

X-12-ARIMA provides the following seasonal filter options: 3x1, 3x3, 3x5, 3x9, 3x15 and stable. (A stable seasonal filter uses all the values for the particular month or quarter.) You can specify a particular seasonal filter for every month or quarter or possibly different filters in different months, or you can let X-12-ARIMA choose the filter for you.

X-12-ARIMA has built-in procedures to choose the filter length for you based on the Global Moving Seasonality Ratio (MSR) which is a measure of the average change in the irregular divided by the average change in the seasonal for the entire series. The most common choice for the seasonal filter is 3x5.

If the MSR is small, then that indicates, generally speaking, that either the irregular is small or the seasonal pattern is changing (or perhaps both). In a situation like this, X-12-ARIMA will choose a short filter (3x3) to pick up the changes in the seasonal pattern. If the MSR is large, that indicates that either the irregular is large or that the seasonal pattern is stable, or perhaps both. For a large MSR, X-12-ARIMA will choose a longer filter (3x9) to fit the more stable seasonal pattern and to be more stable in the presence of extreme values.

For more on seasonal filters, see X-12-ARIMA Reference Manual.

8.     What trend filters are available in X-12-ARIMA?

In X-12-ARIMA a simple 2x12 (or 2x4 for quarterly series) trend filter is used for the first rough estimate of the trend. The other trend-cycle estimates come from very complex filters known as Henderson filters. Henderson filters are designed to be able to estimate curves.

For monthly series X-12-ARIMA will choose either a 9-, a 13-, or a 23-term Henderson filter automatically, based on the I/C ratio. X-12-ARIMA chooses a 9-term Henderson filter when the I/C ratio is small to pick up the changes in the trend-cycle and chooses a 23-term filter when the I/C ratio is large so that the filter is less susceptible to extreme values. For quarterly series, X-12-ARIMA will choose either a 5-term or a 7-term Henderson filter. If you prefer, you can specify the Henderson moving average of any odd number greater than one and less than or equal to 101.

For more on trend filters, see X-12-ARIMA Reference Manual.

9.     How does X-12-ARIMA handle extreme values?

X-12-ARIMA has two separate procedures to handle extreme values.

If a point is a very large point outlier or a shift in the level of the series, the effect is estimated as a regression effect in the regARIMA model and prior-adjusted out of the series before the iterative procedures begin.

As part of the iterative procedure, there is another estimation of points that are unusual and may cause problems. These points are identified by comparing the standard deviation of the irregular to individual points of the irregular component. Any points that are too far away from the identity (1 for multiplicative adjustments or 0 for additive adjustments) is downweighted. These points are called extreme values in the X-12-ARIMA output (found in Table D9). This procedure would only identify point outliers, not shifts in the level of the series as we could define with the regARIMA model.

Outliers and extreme values are adjusted out of the series when estimating the seasonal component so that they don't affect the estimate of the seasonal component. However, they are not adjusted out of the seasonally adjusted series. Point outliers and extreme values are included with the irregular component. Level shifts are included with the trend component. Because the seasonally adjusted series is the trend and irregular components together, all outliers and extreme values are included in the seasonally adjusted series.

10.     What if I have a holiday not included in X-12-ARIMA?

X-12-ARIMA includes built-in regressors for Easter, Labor Day, and Thanksgiving. You can estimate other holiday effects by defining them as user-defined regressors in the regARIMA model.

There is a utility program available to help users generate user-defined regressors for holidays. A DOS version is available from the US Census Bureau, and a Windows version will be available here soon.

11.     How do I process a large number of series?

X-12-ARIMA allows for running a large number of series in two "batch" modes:

• multi-spec mode , where there is an input specification file for every series, and
• single-spec mode , where every series will run with the options from a single input specification file.

For production purposes, it is best to use multi-spec mode. (Single-spec mode can be useful for research purposes in particular.)

To run X-12-ARIMA on more than one series, you first have to create a file with a list of all the series you need to run. For multi-spec mode, the file is called a metafile and simply lists the input specification files to run without the .spc extension on the file names. A metafile must have the extension .mta.

The Windows Interface to X-12-ARIMA can create metafiles as well as spec files.

12.     How do I get X-12-ARIMA to calculate an adjustment for the total of my series?

X-12-ARIMA is able to compute both direct and indirect adjustments of the aggregate or composite series from a set of component series. (A direct adjustment is when we combine the component series first and then adjust. An indirect adjustment is when the component series are seasonally adjusted first and then combined to get an adjustment for the total.)

There are four steps when asking X-12 to calculate a seasonal adjustment for an aggregate series:

13.     How do I get updates and/or more information on X-12-ARIMA?

14.     Is there training available?

The US Census Bureau teaches a limited number of courses, mostly to government and nonprofit organizations.

15.     How do I get started? How do I write all the input files I need? How do I find out about the diagnostics?

Once you've read the FAQ and the Getting Started papers (and registered for a class), you should try to run X-12-ARIMA on your own. There are several resources that can help.

The first step is to get your data in a form that X-12-ARIMA can read. If your data is in Excel files, then X-12-Data can help. For more information on X-12-ARIMA formats, please see the Getting Started paper.

The Windows Interface to X-12-ARIMA will write input specification files and metafiles for you. If you are a new user, this program will help you tremendously.

Once you've run the program, if you need a system to help manage the diagnostics, there is software for that also -- a SAS program and Excel macro to manage the diagnostics are called X-12-Rvw and X-12-Rvw-Excel.

16.     Can I run X-12-ARIMA from Excel?

No. Currently there is no DLL for X-12-ARIMA to run it from Excel. However, there is a program called that is an Excel macro for converting your Excel spreadsheets into data files that X-12-ARIMA can read. Then you can run the Windows version of X-12-ARIMA.

17.     When will X-13 be available and what will it be like?

There is a new program still in beta testing that will combine the current filters used in X-12-ARIMA with model-based adjustment similar to the program SEATS. In SEATS, the seasonal and trend filters are based on the ARIMA model. The new program will still provide access to all of X-12-ARIMA’s seasonal and trend filters and diagnostics.

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