应用时间序列分析 -
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
姓名：葛国峰学号：1122307851 编号：33 习题2.3
2.解：
data b;
input y@@;
time=intnx('month','1jan1975'd,_n_-1);
format time data;
cards;
330.45 330.97 331.64 332.87 333.61 333.55
331.90 330.05 328.58 328.31 329.41 330.63
331.63 332.46 333.36 334.45 334.82 334.32
333.05 330.87 329.24 328.87 330.18 331.50
332.81 333.23 334.55 335.82 336.44 335.99
334.65 332.41 331.32 330.73 332.05 333.53
334.66 335.07 336.33 337.39 337.65 337.57
336.25 334.39 332.44 332.25 333.59 334.76
335.89 336.44 337.63 338.54 339.06 338.95
337.41 335.71 333.68 333.69 335.05 336.53
337.81 338.16 339.88 340.57 341.19 340.87
339.25 337.19 335.49 336.63 337.74 338.36
;
run;
proc gplot;
plot y*time;
symbol1v=dot i=join c=black w=3;
proc arima data=b;
identify var=y nlag=24;
run;
（1）序列图：
判断：由图形可知：该序列不平稳。
（2）
The SAS System 10：20 Tuesday, September 20, 2013 1
The ARIMA Procedure
WARNING: The value of NLAG is larger than 25% of the series length. The asymptotic approximations used for correlation based statistics and confidence intervals may be poor.
Name of Variable = y
Mean of Working Series 334.5044
Standard Deviation 3.151627
Number of Observations 72
Autocorrelations
Lag Covariance Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1 Std Error
0 9.932752 1.00000 | |********************| 0
1 9.014050 0.90751 | . |****************** | 0.117851
2 7.168604 0.72171 | . |************** | 0.191744
3 5.090716 0.51252 | . |********** | 0.226350
4 3.474700 0.34982 | . |******* . | 0.241932
5 2.452361 0.24690 | . |***** . | 0.248858
6 2.017285 0.20309 | . |**** . | 0.252237
7 2.087944 0.21021 | . |**** . | 0.254498
8 2.625108 0.26429 | . |***** . | 0.256898
9 3.618821 0.36433 | . |******* . | 0.260647
10 4.814571 0.48472 | . |**********. | 0.267627
11 5.806306 0.58456 | . |************ | 0.279554
12 5.979308 0.60198 | . |************ | 0.296045
13 5.149264 0.51841 | . |********** . | 0.312584
14 3.660844 0.36856 | . |******* . | 0.324305
15 2.053220 0.20671 | . |**** . | 0.330072
16 0.808334 0.08138 | . |** . | 0.331865
17 0.013455 0.00135 | . | . | 0.332142
18 -0.322607 -.03248 | . *| . | 0.332142
19 -0.269167 -.02710 | . *| . | 0.332186
20 0.111604 0.01124 | . | . | 0.332217
21 0.821916 0.08275 | . |** . | 0.332222
22 1.689632 0.17011 | . |*** . | 0.332508
23 2.415631 0.24320 | . |***** . | 0.333715
24 2.508248 0.25252 | . |***** . | 0.336167
"." marks two standard errors
（3）The SAS System 10：20 Tuesday, September 20, 2013 2
The ARIMA Procedure
Inverse Autocorrelations
Lag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1
1 -0.5808
2 | ************| . |
2 -0.02712 | . *| . |
3 0.20008 | . |****. |
4 -0.12866 | . ***| . |
5 0.07658 | . |** . |
6 -0.0619
7 | . *| . |
7 0.03185 | . |* . |
8 0.02403 | . | . |
9 -0.10454 | . **| . |
10 0.17130 | . |*** . |
11 -0.12291 | . **| . |
12 -0.00765 | . | . |
13 0.04289 | . |* . |
14 -0.05806 | . *| . |
15 0.11307 | . |** . |
16 -0.10786 | . **| . |
17 0.02081 | . | . |
18 0.06299 | . |* . |
19 -0.06869 | . *| . |
20 0.02879 | . |* . |
21 -0.03841 | . *| . |
22 0.09736 | . |** . |
23 -0.09477 | . **| . |
24 0.03281 | . |* . |