PROC X12: Basic Seasonal Adjustment :: SAS/ETS(R) 9.2 User's Guide

The X12 Procedure

Suppose that you have monthly retail sales data starting in September 1978 in a SAS data set named SALES. At this point, you do not suspect that any calendar effects are present, and there are no prior adjustments that need to be made to the data.

In this simplest case, you need only specify the DATE= variable in the PROC X12 statement and request seasonal adjustment in the X11 statement as shown in the following statements:

   data sales;
      set sashelp.air;
      sales = air;
      date = intnx( 'month', '01sep78'd, _n_-1 );
      format date monyy.;
   run;

   proc x12 data=sales date=date;
      var sales;
      x11;
      ods select d11;
   run ;

The results of the seasonal adjustment are in table D11 (the final seasonally adjusted series) in the displayed output as shown in Figure 32.1.

Figure 32.1 Basic Seasonal Adjustment

The X12 Procedure

Table D 11: Final Seasonally Adjusted Data
For variable sales
Year	JAN	FEB	MAR	APR	MAY	JUN	JUL	AUG	SEP	OCT	NOV	DEC	Total
1978	.	.	.	.	.	.	.	.	124.560	124.649	124.920	129.002	503.131
1979	125.087	126.759	125.252	126.415	127.012	130.041	128.056	129.165	127.182	133.847	133.199	135.847	1547.86
1980	128.767	139.839	143.883	144.576	148.048	145.170	140.021	153.322	159.128	161.614	167.996	165.388	1797.75
1981	175.984	166.805	168.380	167.913	173.429	175.711	179.012	182.017	186.737	197.367	183.443	184.907	2141.71
1982	186.080	203.099	193.386	201.988	198.322	205.983	210.898	213.516	213.897	218.902	227.172	240.453	2513.69
1983	231.839	224.165	219.411	225.907	225.015	226.535	221.680	222.177	222.959	212.531	230.552	232.565	2695.33
1984	237.477	239.870	246.835	242.642	244.982	246.732	251.023	254.210	264.670	266.120	266.217	276.251	3037.03
1985	275.485	281.826	294.144	286.114	293.192	296.601	293.861	309.102	311.275	319.239	319.936	323.663	3604.44
1986	326.693	330.341	330.383	330.792	333.037	332.134	336.444	341.017	346.256	350.609	361.283	362.519	4081.51
1987	364.951	371.274	369.238	377.242	379.413	376.451	378.930	375.392	374.940	373.612	368.753	364.885	4475.08
1988	371.618	383.842	385.849	404.810	381.270	388.689	385.661	377.706	397.438	404.247	414.084	416.486	4711.70
1989	426.716	419.491	427.869	446.161	438.317	440.639	450.193	454.638	460.644	463.209	427.728	485.386	5340.99
1990	477.259	477.753	483.841	483.056	481.902	499.200	484.893	485.245	.	.	.	.	3873.15
Avg	277.330	280.422	282.373	286.468	285.328	288.657	288.389	291.459	265.807	268.829	268.774	276.446

Total: 40323 Mean: 280.02 S.D.: 111.31
Min: 124.56 Max: 499.2

You can compare the original series (Table A1) and the final seasonally adjusted series (Table D11) by plotting them together as shown in Figure 32.2. These tables are requested in the OUTPUT statement and are written to the OUT= data set. Note that the default variable name used in the output data set is the input variable name followed by an underscore and the corresponding table name.

   proc x12 data=sales date=date noprint;
      var sales;
      x11;
      output out=out a1 d11;
   run;
   
   proc sgplot data=out;
      series x=date y=sales_A1 / name = "A1" markers
                                 markerattrs=(color=red symbol='asterisk')
                                 lineattrs=(color=red);
      series x=date y=sales_D11 / name= "D11" markers 
                                  markerattrs=(symbol='circle')
                                  lineattrs=(color=blue);
      yaxis label='Original and Seasonally Adjusted Time Series';
   run;

Figure 32.2 Plot of Original and Seasonally Adjusted Data

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