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 38.1.
Figure 38.1: Basic Seasonal Adjustment
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 |
You can compare the original series (Table A1) and the final seasonally adjusted series (Table D11) by plotting them together as shown in Figure 38.2. 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 38.2: Plot of Original and Seasonally Adjusted Data