SAS/ETS 9 22 User''''''''s Guide 33 pptx
SAS/ETS 9.22 User''''s Guide 33 pptx
... Technometrics, 22, 3 89 396 .
Kohn, R. and Ansley, C. ( 198 5), “Efficient Estimation and Prediction in Time Series Regression
Models,” Biometrika, 72, 3, 694 – 697 .
Ljung, G. M. and Box, G. E. P. ( 197 8), “On ... Models,” JASA, 79 (385),
84 96 .
Tsay, R. S. and Tiao, G. C. ( 198 5), “Use of Canonical Analysis in Time Series Model Identification,”
Biometrika, 72 (2), 299 –315.
Woodfield, T....
SAS/ETS 9.22 User''''s Guide 8 pptx
... 199 0 6 1 29. 9
2 199 0 7 130.4
3 199 0 8 131.6
4 199 0 9 132.7
5 199 0 10 133. 5
6 199 0 11 133. 8
7 199 0 12 133. 8
8 199 1 1 134.6
9 199 1 2 134.8
10 199 1 3 135.0
11 199 1 4 135.2
12 199 1 5 135.6
13 199 1 ... 11231 01OCT 199 0
6 NOV 199 0 133. 8 11262 01NOV 199 0
7 DEC 199 0 133. 8 11 292 01DEC 199 0
8 JAN 199 1 134.6 11323 01JAN 199 1
9 FEB 199 1 134.8 11354 01FEB 199 1
10 MAR 199...
SAS/ETS 9.22 User''''s Guide 13 pptx
... cpi
37 JUN 199 1 ACTUAL 0 136.000
38 JUN 199 1 FORECAST 0 136.146
39 JUN 199 1 RESIDUAL 0 -0.146
40 JUL 199 1 ACTUAL 0 136.200
41 JUL 199 1 FORECAST 0 136.566
42 JUL 199 1 RESIDUAL 0 -0.366
43 AUG 199 1 FORECAST ... CPI
1 JAN90 Chicago 128.1
2 FEB90 Chicago 1 29. 2
3 MAR90 Chicago 1 29. 5
4 APR90 Chicago 130.4
5 MAY90 Chicago 130.4
6 JUN90 Chicago 131.7
7 JUL90 Chicago 132.0
8 JAN90 Los_Ang...
SAS/ETS 9.22 User''''s Guide 19 pptx
... restart update method of Powell ( 197 7) and Beale ( 197 2).
FR
performs the Fletcher-Reeves update (Fletcher 198 7).
PR
performs the Polak-Ribiere update (Fletcher 198 7).
CD
performs a conjugate-descent ... approximation. The trust region method is implemented using Dennis,
Gay, and Welsch ( 198 1), Gay ( 198 3), and Moré and Sorensen ( 198 3).
The trust region method performs well fo...
SAS/ETS 9.22 User''''s Guide 20 pptx
... Mei, H.H.W. ( 197 9), “Two New Unconstrained Optimization Algorithms Which
Use Function and Gradient Values,” J. Optim. Theory Appl., 28, 453–482.
Dennis, J.E. and Schnabel, R.B. ( 198 3), Numerical ... E.M.L. ( 197 2), “A Derivation of Conjugate Gradients,” in Numerical Methods for Nonlinear
Optimization, ed. F.A. Lootsma, London: Academic Press.
Dennis, J.E., Gay, D.M., and Welsch, R.E. ( 1...
SAS/ETS 9.22 User''''s Guide 52 pptx
... 1 1 JAN 198 22 090 255 24368 -227 8 -9. 348
1 1 1 FEB 223 26830 217 20104 6726 33. 456
1 1 1 MAR 1 19 14020 210 194 05 -5385 -27.751
1 1 1 QTR1 540 6 294 0 682 63877 -93 7 -1.467
1 1 1 APR 82 98 60 162 ... 16374 -6514 - 39. 783
1 1 1 MAY 180 2 1330 67 6325 15005 237. 233
1 1 1 JUN 183 21060 124 1 2333 8727 70.761
1 1 1 QTR2 445 5225 0 353 35032 17218 49. 1 49
1 1 1 JUL 194 23210 99 1...
SAS/ETS 9.22 User''''s Guide 78 pptx
... Converting Observation Characteristics ✦ 7 69
characteristics of the series. The observation characteristics of the series are specified with ... spaced points in time.
proc expand data=a out=b;
run;
Refer to the section “Missing Values” on page 792 for further information.
Requesting Different Interpolation Methods
By default, a cubic spline ... . . . . . 768
Converting Observation Character...
SAS/ETS 9.22 User''''s Guide 92 pptx
... 0.00
2 108561.77 0.0 5221 2 130 397 .88 130788.65
3 118182. 19 0.087784 161810.00 75 798 . 19
4 107015.58 0.053231 13 195 5 .90 125011.88
5 10 790 6.61 0.052107 130242.56 138063.41
6 578 39. 44 0.051424 137741.07 ... Paid
8.25% - no discount 95 847.27 106164 .97 751.27 36415.85
points
8% - 1 discount point 95 656 .22 106153 .97 733. 76 352 79. 26
Loan Comparison Report
Analysis through J...
SAS/ETS 9.22 User''''s Guide 105 pptx
... covariance matrix is singular, the parameter estimates are not unique. Refer to Noble and
Daniel ( 197 7, pp. 337 –340) for more details about generalized inverses.
GENGMMV
specify GMM variance under arbitrary ... Estimates
Approx Approx
Term Estimate Std Err t Value Pr > |t| Label
Join point 12.7504 1.2785 9. 97 <.0001 x0
plateau 0.777516 0.0123 63.10 <.0001 a + b
*
x0 + c
*
x0
**...
SAS/ETS 9.22 User''''s Guide 120 pptx
... 0.000000 ERROR.demand 0.000000
Solution Values
price
33. 590 16
The output indicates that it took six Newton iterations to determine the PRICE of 33. 590 2, which
makes the DEMAND value within 16E–11 ... data=sashelp.citimon;
parameters a 0.010708 b -0.4788 49 c 0 .92 9304;
lhur= 1/(a
*
ip) + b + c
*
lag(lhur) ;
solve lhur / out=sim dynamic stats theil;
range date to '01nov91'd;...