# The X12 Procedure

### Basic Seasonal Adjustment

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 shown in Figure 37.1.

Figure 37.1: 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 37.2. 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 37.2: Plot of Original and Seasonally Adjusted Data