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I found this code online and I wanted to dissect it before programming something similar in SAS.

The problem is idenitfy seasonality in a time series. I want to understand the code line by line:

proc x12 data=sales date=date season=12;
      var unempl_rate *already log differenced;
      transform function=none;
      regression predefined=td;
      automdl maxorder=(1,1)
              print=unitroottest unitroottestmdl autochoicemdl best5model;
      estimate;
      x11;
      output out=out(obs=23) a1 d11 d18;
   run;
   proc print data=out(obs=23);
      title 'Output Variables Related to Trading Day Regression';
   run;

By line:

  1. the first line just defined use the x12 procedure with data set called "sales" - monthly data, indexed by variable "date" and expect a seasonality in the data every 12 months.
  2. this line tells which variable to analyze.
  3. no transformation was necessary since a transformation was already done.
  4. this line confuses me a little, is "regression predifined=td" applied for adjustment/outliers/events?

5-7. do these lines mean to estimate the best ARIMA (linear) models on the residual? 8-onwards. For outputing results

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You can find a thorough description of the methodology at Census web site, here. The current routine is X-13ARIMA-SEATS, it's very popular beyond Census Bureau.

In old days they used X-13. If you have annual seasonality in monthly series, then this is the way to go. They supply the routine for download. They have a ton of references on the subj.

MATLAB has a few examples with code and detailed explanations, e.g. see here

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