X12 ARIMA in NumXL Notes

X12‐ARIMA Tutorial ‐1‐ © Spider Financial Corp, 2013

TechnicalNote:NumXLX12‐ARIMA

Starting with version 1.57, NumXL will support U.S. Census X‐12‐ARIMA modeling including seasonal

adjustment, trend filtering, and model identification and forecasting.

In this paper, we will go over the approach followed by NumXL to implement this model.

OverviewThe approach of this feature is to use the US Census free program (aka x12a.exe), which provides users

with complete excel interface as well as raw input and output files for advanced users.

The US Census X12‐ARIMA program possesses no user interface. To invoke it, the user needs to write a

script input file (aka specification) along with the data and invoke the program from the command line

interface. Once complete, the program generates various output messages and files in designated

folders.

To put an new Excel face on this widely‐used legacy program, NumXL provides a wizard or dialog box

which users can use to specify the cells range of their data, select various modeling options, save those

settings as part of their Excel spreadsheet and query the different outputs of their model.

Behind the scenes, NumXL transforms the user’s data and model selections into a native x12a

specification file. Finally, NumXL runs the x12a program, reads the output files and avails the results to

the user in excel.


The whole process (i.e. preparing the specification file, running the program and reading output files) is

hidden from the user, but to promote transparency, the NumXL X12‐ARIMA wizard allows access to

different input/output files (e.g. specification file, error file and output file).

NOTE: During the NumXL installation, the installer program copies the x12A program (32/64‐bit version)

and all support files into your computer under the NumXL home path. The user is not required to

download it from the US Census website, and in the event that the user already has this program,

NumXL uses the program that comes with the installer to avoid any version mismatch issues.

DataPreparationSimilar to what we did in our earlier tutorial, we organize our sample data by placing the date in one

column and the variable values in a separate column, with each observation in a separate row.

In the example above, we used the real quarterly GDP data for US, France, Australia and Canada.

Please note:

1. The different time series do not start on the same date. We replace the missing values with

#N/A.


2. The different time series may not finish on the same date.

3. They are all quarterly data. They do not mix between monthly and quarterly data.

Furthermore, users may add future dates to the end of the time series and fill it in with “#N/A” for their

future values (see following figure).

NumXL removes the missing values from either end of the times series and adjusts the series start date,

so it is not an issue. For the user, the new row serves as a place holder for future observations; so as

new data become available (published), the user replaces the missing value (i.e. #N/A) with the actual

values, triggering the models formulae to re‐evaluate, without the need to edit anything in the

spreadsheet.

IMPORTANT: The X12A program has a few hard limits on the size of the time series:

1. The maximum length of a time series is 600 observations

2. The minimum length of monthly time series is 3 years ( 36 observations)

3. The minimum length of quarterly time series is 4 years (16 observations)

To accommodate those limits for larger time series, NumXL picks the most recent 600 observations and

adjusts the start date of the series accordingly.

ProcessFirst, select an empty cell in your worksheet to store the unique identifier or your X12‐ARIMA in Excel.


Next, Locate the X12 ARIMA icon in the toolbar (or menu in Excel 2003) and click on it.

The X12 ARIMA Wizard (dialog‐box) in Excel appears.


For the input time series data, select the cells range for the values, start date and the frequency of the

observations (i.e. monthly or quarterly).

Note:

1. The values of the selected cells range may contain missing values (#N/A) at either end.

2. The time series may not contain any intermediate missing values. If your series has one or more

intermediate missing value(s), substitute a fill‐in value using interpolation or any method you

are comfortable with.

3. The start date is a valid Excel date (e.g. 1/1/1947) even for quarterly data. Don’t use other

formats similar to 1947.Q1 or 1947.3, as those are not valid dates in Excel.


4. The start date must correspond to the first observation in the time series regardless of whether

the observation’s value is missing or not.

Next, let’s set the prior‐adjustment of our input data:

This section allows you to set a special data treatment prior to the modeling process. For instance,

“Transform” instructs the X12a program to model the log‐values of our time series.

In the regression section, user can adjust for special calendar effects such as trading days and holidays

like Easter. For more details on calendar effects and adjustment, refer to our document online:

1. Calendar Effects

http://www.spiderfinancial.com/support/documentation/numxl/tips‐and‐tricks/calendar‐

effects

IMPORTANT: The Easter holiday effect commences N days before Easter. Currently, NumXL uses 14

days prior to Easter. This will be changed in future releases to permit user to select a value.

In the outlier type section, user can select which types of outliers to detect and adjust for. For more

details on those types of outliers, refer to our document online:

1. Data preparation ‐ Outliers:

http://www.spiderfinancial.com/support/documentation/numxl/tips‐and‐tricks/data‐

preparation‐outliers

Now, let’s examine the ARIMA model section:


The X12‐ARIMA methodology (regARIMA) uses a seasonal ARIMA (SARIMA) model to capture both the

seasonality (deterministic) and the (stochastic) cyclicity in the data.

The user may elect for the program to find the best fit model (“Auto Select”) or they can specify the

order of the model.

For more information about time series decomposition and/or seasonal adjustment, refer to our online

document.

1. Patterns Unplugged: http://www.spiderfinancial.com/support/documentation/numxl/tips‐and‐

tricks/patterns‐unplugged

In the “Forecast” section, we can select the duration of the forecast. It is set to one (1) year by default,

but users can select a higher forecast horizon up to seven (7) years (hard limit).

Now, let’s set the seasonal adjustment values:

By default, the X11 seasonal adjustment option is selected. The X11 filter is derived from Henderson

trend filters (Robert Henderson 1916).

In X11 Mode, the user can control the type of seasonal adjustment decomposition calculated (mode):

multiplicative, additive, pseudo‐additive or log additives.


Using the X11 filter option, the user can control the seasonal moving average used. Currently, the trend

moving average is set to 13.

Note:

1. The Trading Day effect and other holiday adjustments in X11 are not yet available in NumXL.

2. The Extreme value adjustment control is enabled and set to sigma limit of 1.25 and 2.75.

For more information about time series decomposition and/or seasonal adjustment, refer to our online

document.

1. Patterns Unplugged: http://www.spiderfinancial.com/support/documentation/numxl/tips‐and‐

tricks/patterns‐unplugged

Now that we’ve finished specifying the model options, click “Apply.”


Notes:

1. The “Open X‐12 SPC file” button becomes enabled.

2. If you click on “Open X‐12 SPC file,” the Windows Notepad application will launch with the x12a

specification file opened.

3. In the selected cell in your worksheet, the X12‐ARIMA generates a unique identifier for the

model.

4. The “Run X12A” button is enabled now.


Finally, let’s run the x12a program. Click the “Run X‐12A” button. NumXL invokes the program and

passes the specification file generated earlier. Upon completion, a dialog box pops up.

Click “OK.”

Note that all command buttons on the upper right corner of the dialog are now enabled.


Let’s examine the status (i.e. warnings or errors) produced by the x12a program execution. Click “Open

X‐12 Error File” to view the file.

Again, the Notepad application is launched and the error file generated by the x12a program is

displayed.

Note:

1. The error file (x12a_34cc1761.err) has the same base file name as the specification file name

(i.e. x12a_34cc1761.spc), which is the unique identifier of the x12‐arima model

(x12a_34cc1761).

2. In the case of US real GDP, the x12a did not detect any significant seasonality, thus it throws a

warning.

3. The second paragraph of the warning above is not relevant to our case, as we model the gross

GDP time series (rather than its GDP components (e.g. consumption, investment, government

spending and import/export)).

Optionally, to may examine the raw x12a output. Click on the “Open X12 Output file” button and, again,

the Notepad application is launched and the x12 output file is displayed.


Notes:

1. A series of tests for seasonality is performed first. In the case of the US real GDP series, the test

did not find any significant seasonality.

2. The auto‐modeling regARIMA procedure prints out the order (AR & MA) of the selected model.

In our case, it is (1 1 1) with no seasonality (i.e. ARIMA(1,1,1))

Now, click “OK” to exist the wizard.

OutputsBy now you must be wondering, “where are the model’s outputs?” NumXL offers a few worksheet

functions to query the model’s different outputs.


To start, let’s query the X11 seasonally adjusted time series. Use the X12ACOMP function for this

purpose.

Notes:

1. The first argument references the model’s unique identified in cell B1.

2. The second argument references the step from the beginning of the time series, so for C3, the

step is equal to one (1).

3. The step value ranges between one (1) and the length of the input time series. For our US GDP

quarterly data example, the step can be between one (1) and 265.

4. The last argument selects the output component. For X11 seasonally adjusted (SA), select one.

5. Refer to the X12ACOMP reference manual page for more details.

For forecasting, we use the X12AFORE to query the forecast value and/or confidence interval.

Notes:


1. The first argument references the model’s unique identified in cell B1.

2. The second argument references the step from the end of the time series (last non‐missing

value), so for K268, the step is equal to one (1).

3. The step value ranges between one (1) and the forecast horizon. For our US GDP quarterly data

example, the step can be between one (1) and four (1 year). After that, the X12AFORE returns

the last known forecast value (e.g. Q4).

4. For forecast mean value, the 3rd argument is set to one (1).

5. Refer to the X12AFORE reference manual page for more details.

ConclusionIn this tutorial, we demonstrated the process to model an X12‐ARIMA model and derive an X11

seasonally adjusted time series in Excel using NumXL’s add‐in functions.

Throughout the tutorial, we presented several elements of NumXL’s implementation of X12‐ARIMA, in

an attempt to help you resolve issues that may pop up during the modeling process.

Wheredowegofromhere?First, to answer the question of optimality, we need to introduce additional algorithms to select the

optimal set options and their values (e.g. X11 filter options, X11 mode, etc.) for a given data set.

Second, the set of calendar holidays supported is relatively limited. By combining the calendar functions

in NumXL, we can expand the set significantly.

1. Furthermore, we are planning to add non‐fixed holidays such as Chinese New Year, as well as

Islamic and Jewish holidays.

2. Full support for user‐defined (exogenous) explanatory/regression variables.

Third, many of the economic data can be represented as a sum of their components (e.g. GDP and its

components: consumption, investment, government and import/export net), so modeling the

components and their sum requires special handling.

Finally, the US Census will release the X13‐ARIMA‐SEATS edition of their program, so new filtering

option will become available.

Documents

X12 ARIMA in NumXL Notes