SAS/ETS 9 22 User''''''''s Guide 195 pps
SAS/ETS 9.22 User''''s Guide 195 pps
... 0.00023436 0.00010 79 2.17 0.0 298 Level Error Variance 0.000 298 28 0.0001057 2.82 0.0048 Slope Error Variance 8.4 791 1E-13 6 .227 1E-10 0.00 0 .99 89 Season Error Variance 0.00000356 1.32347E-6 2. 69 0.0072 The ... Error 0.54132 Maximum Percent Error 2. 190 97 R-Square 0 .99 061 Adjusted R-Square 0 .99 046 Random Walk R-Square 0.87288 Amemiya's Adjusted R-Square 0 .99 017 Number o...
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