应用时间序列分析 -.doc

姓名：葛国峰 学号： 1122307851 编号：33 习题2.32.解：data b;input y;time=intnx(month,1jan1975d,_n_-1);format time data;cards;330.45 330.97 331.64 332.87 333.61 333.55331.90 330.05 328.58 328.31 329.41 330.63331.63 332.46 333.36 334.45 334.82 334.32333.05 330.87 329.24 328.87 330.18 331.50332.81 333.23 334.55 335.82 336.44 335.99334.65 332.41 331.32 330.73 332.05 333.53334.66 335.07 336.33 337.39 337.65 337.57336.25 334.39 332.44 332.25 333.59 334.76 335.89 336.44 337.63 338.54 339.06 338.95337.41 335.71 333.68 333.69 335.05 336.53337.81 338.16 339.88 340.57 341.19 340.87339.25 337.19 335.49 336.63 337.74 338.36;run;proc gplot;plot y*time;symbol1 v=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 AutocorrelationsLag 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 -0.58082 | *| . | 2 -0.02712 | . *| . | 3 0.20008 | . |*. | 4 -0.12866 | . *| . | 5 0.07658 | . |* . | 6 -0.06197 | . *| . | 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 | . |* . | Partial 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 0.90751 | . |* | 2 -0.57732 | *| . | 3 0.02855 | . |* . | 4 0.23812 | . |* | 5 -0.03355 | . *| . | 6 0.06915 | . |* . | 7 0.15640 | . |* . | 8 0.17931 | . |*. | 9 0.25748 | . |* | 10 0.10993 | . |* . | 11 0.00617 | . | . | 12 -0.25943 | *| . | 13 -0.17679 | .*| . | 14 0.02902 | . |* . | 15 -0.03960 | . *| . | 16 0.01081 | . | . | 17 -0.09768 | . *| . | 18 -0.02402 | . | . | The SAS System 10：20 Tuesday, September 20, 2013 3 The ARIMA ProcedurePartial Autocorrelations Lag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1 19 0.00630 | . | . | 20 -0.08454 | . *| . | 21 0.06247 | . |* . | 22 0.01467 | . | . | 23 0.02958 | . |* . | 24 -0.10605 | . *| . | Autocorrelation Check for White Noise To Chi- Pr Lag Square DF ChiSq -Autocorrelations- 6 139.50 6 .0001 0.908 0.722 0.513 0.350 0.247 0.203 12 242.38 12 .0001 0.210 0.264 0.364 0.485 0.585 0.602 18 283.85 18 .0001 0.518 0.369 0.207 0.081 0.001 -0.032 24 301.25 24 Lag Square DF ChiSq -Autocorrelations-6 6.56 6 0.3634 0.052 -0.048 -0.108 -0.218 -0.1