Story Name: Predicting Retail Sales

Abstract:

The datafile contains 11 years of quarterly sales for four kinds of retail establish-
ments, along with non-agricultural employment and wage and salary disbursements
The task is to develop a model for predicting sales using leading values of employ-
ment or wage and salary disbursements, seasonal indicators or seasonal lags, other
lags of the dependent variable, and time. If significant positive autocorrelation is
present in the residuals from the best model, a revised model can be obtained by
introducing lagged values of the residuals into the model as an additional predictor.

Use a prediction routine to obtain predictions for the four quarters of 1990 from the
best model, and the 1st quarter of 1990 for the model if modified by use of lagged
residuals. Compare the prediction with the actual value(s) for 1990 given below. Note
whether the 95% confidence intervals for the prediction(s) include the actual value(s).

Values for 1990:

Quarter BDLG AUTO FURN GMER

1 19368 92253 21738 41832
2 26220 103038 22842 50181
3 99006 22620 49137 34911
4 87063 25611 71265 36543

Reference: U.S. Department of Commerce, Survey of Current Business
Authorization: free use
Description:

These data are published monthly in the statistical section of the Survey of
Current Business.

Number of cases: 44
Variable Names:

1.1. TIME: Quarter, from 1st quarter 1979 to 4th quarter 1989
2.WASA: National income wage and salary disbursements ($ billions)
3.EMPL: Employees on payrolls of non-agricultural establishments (thousands)
4.BLDG: Building material dealer sales ($ millions)
5.AUTO: Automotive dealer sales ($ millions)
6.FURN: Furniture and home furnishings dealer sales ($ millions)
7.GMER: General merchandise dealer sales ($ millions)

The Data:

TIME WASA EMPL BDLG AUTO FURN GMER

1 1193.3 87973 9392 42960 9203 30363
2 1217.3 90021 13637 47516 10174 25812
3 1254.4 90120 14392 43888 11078 26026
4 1284.7 91180 13301 40298 12334 38081
5 1319.8 89832 10411 40016 10228 21909
6 1335.1 90668 13057 39073 10394 26615
7 1360.1 89850 13621 40484 11246 27039
8 1411.6 91276 13354 38703 13071 40170
9 1451.7 89964 11056 41919 10954 23641
10 1478.1 91566 14559 45398 11410 30477
11 1512.6 91405 14067 46635 11892 30065
12 1530.6 91691 12286 39970 13204 43805
13 1542.7 89341 9658 41554 10439 24256
14 1563.9 90310 13659 48384 11148 30976
15 1579.8 89324 12351 46041 11556 30579
16 1586.0 89436 12307 46411 13370 45471
17 1610.7 88815 10508 47558 11114 25891
18 1643.5 89920 16287 59342 12043 33050
19 1671.3 90480 16697 57487 13165 33385
20 1715.4 92367 15523 57165 15534 50311
21 1755.8 91637 13540 62200 13002 29177
22 1793.1 94256 19190 74678 14602 36855
23 1819.5 95021 18827 69074 15684 35418
24 1847.7 96547 17215 67030 19088 53126
25 1882.7 95450 13848 70453 15436 30325
26 1939.8 97657 20319 85102 16117 37977
27 1976.0 97985 20403 85142 16782 37028
28 2012.8 99383 19179 71203 20491 54369
29 2044.1 98213 16790 73380 16383 31594
30 2069.8 99850 24400 88139 18180 37617
31 2097.9 99877 24215 92899 19782 36297
32 2128.4 101169 22042 81838 23605 52457
33 2163.2 99922 18182 75122 18483 30718
34 2211.9 102186 22670 90515 19659 41090
35 2265.1 102657 21204 89907 21042 40706
36 2325.0 104522 19746 77163 24203 60491
37 2358.7 103445 16656 84508 20242 34723
38 2405.6 106050 24552 99279 22143 42871
39 2452.2 105963 23775 94556 22870 42072
40 2505.1 107644 22449 87039 27486 64101
41 2560.7 106425 18145 87696 22399 36516
42 2572.9 108741 26344 102934 22412 46782
43 2586.6 108897 25278 102095 22223 47006
44 2612.7 110260 22511 84497 25648 69966