SAS/ETS 9 22 User''''''''s Guide 275 pps
SAS/ETS 9.22 User''''s Guide 275 pps
... setting the hold-out sample to 12 automatically sets the fit range to JUN 197 8–JUL 198 1 and the evaluation range to AUG 198 1–JUL 198 2. If you had set the period of fit and period of evaluation to these ... models now that they have been fit to the period June 197 8 through July 198 1 and evaluated for the hold-out sample period August 198 1 through July 198 2. Note that the fit statistics f...
Ngày tải lên: 02/07/2014, 15:20
SAS/ETS 9.22 User''''s Guide 5 pps
... regression parameters – specification tests Time Series Interpolation and Frequency Conversion ✦ 39 have many series to forecast and you want to extrapolate trends without developing a model for
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 6 ppsx
... (SAS/STAT User’s Guide) . For more information about the SAS Output Delivery system, refer to the SAS Output Delivery System: User’s Guide. Related SAS Software Many features not found in SAS/ETS software ... enable graphical output from SAS/ETS procedures, you must use the following statement in your SAS program. ods graphics on; The graphical output produced by many SAS/ETS procedu...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 10 pps
... cpi FORECAST STD L95 U95 RESIDUAL 1 JUN 199 0 1 29. 9 . . . . . 2 JUL 199 0 130.4 130.368 0.36160 1 29. 660 131.077 0.03168 3 AUG 199 0 131.6 130.881 0.36160 130.172 131. 590 0.7 190 9 4 SEP 199 0 132.7 132.354 ... Chicago JAN90 ACTUAL 0 128.100 2 Chicago JAN90 FORECAST 0 128.252 3 Chicago JAN90 RESIDUAL 0 -0.152 4 Chicago FEB90 ACTUAL 0 1 29. 200 5 Chicago FEB90 FORECAST 0 128. 896 6 Chicag...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 11 ppsx
... max Consumer 1 29. 9 136.2 Price Index * * JUN 199 0 1 29. 90 |c | JUL 199 0 130.40 | c | AUG 199 0 131.60 | c | SEP 199 0 132.70 | c | OCT 199 0 133.50 | c | NOV 199 0 133.80 | c | DEC 199 0 133.80 | c | JAN 199 1 134.60 ... + P | + r | i 132 + c | + e | 131 + I | n | + d 130 + + e | x | 1 29 + + + + + + +- MAY 199 0 AUG 199 0 DEC 199 0 MAR 199 1 JUN 199 1 OCT 199 1 date Using PROC TIMEP...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 23 pps
... Approx Parameter Estimate Error t Value Pr > |t| Lag Variable Shift MU 199 .83602 2 .99 463 66.73 <.0001 0 sales 0 NUM1 -9. 992 99 0.02885 -346.38 <.0001 0 price 0 Any number of input variables ... zero-one indicator for the month of the advertising effort. data a; set a; ad = (date = '1mar 199 2'd); run; proc arima data=a; identify var=sales crosscorr=ad; estimate p=1 q=1...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 25 pps
... statistic is used. If WHITENOISE=ST, a modification of this statistic suggested by Stoffer and Toloi ( 199 2) is used. The default is WHITENOISE=ST. Options for Output Data Sets The following options are ... statistic is used. If WHITENOISE=ST, a modification of this statistic suggested by Stoffer and Toloi ( 199 2) is used. The default is WHITENOISE=ST. ESTIMATE Statement < label: >ESTIM...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 29 pps
... The test statistic is based on the chi-square approximation suggested by Box and Jenkins ( 197 6, pp. 395 – 396 ). The cross-correlation function is computed by using the residuals from the model as ... q=(1)(12) noint method=ml; forecast id=date interval=month; run; Statistical Graphics ✦ 2 79 Figure 7 .22 Forecast Plot of the Airline Model ODS Graph Names PROC ARIMA assigns a name to eac...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 35 pps
... 0.0076 0 .93 04 0.0073 0 .93 19 2 0.0150 0 .99 25 0.0143 0 .99 29 3 0.02 29 0 .99 91 0.0217 0 .99 92 4 0.0308 0 .99 99 0.0 290 0 .99 99 5 0.0367 1.0000 0.0345 1.0000 6 0.0442 1.0000 0.0413 1.0000 7 0.0 522 1.0000 ... 1.6 299 733 Log Likelihood -187.44013 Total R-Square 0 .99 41 SBC 408. 392 693 AIC 388.88025 MAE 0 .97 051406 AICC 3 89. 88025 MAPE 2.7 594 5337 HQC 396 .804343 Normality T...
Ngày tải lên: 02/07/2014, 14:21
SAS/ETS 9.22 User''''s Guide 39 pps
... model (Engle and Ng 199 3), the threshold GARCH (TGARCH) model (Glosten, Jaganathan, and Runkle 199 3; Zakoian 199 4), and the power GARCH (PGARCH) model (Ding, Granger, and Engle 199 3). For more details ... which loses the first observation. This was also demonstrated by Maeshiro ( 197 6), Chipman ( 197 9), and Park and Mitchell ( 198 0). For large samples (Harvey and McAvinchey used 100),...
Ngày tải lên: 02/07/2014, 14:21