姓名：葛国峰学号：1122307851 编号：33 习题2.32.解：data b;input y@@;time=intnx('month','1jan1975'd,_n_-1);format time data;cards;330.45 330.97 331.64 332.87 333.61 333.55331.90 330.05 328.58 328.31 329.41 330.63331.63 332.46 333.36 334.45 334.82 334.32333.05 330.87 329.24 328.87 330.18 331.50332.81 333.23 334.55 335.82 336.44 335.99334.65 332.41 331.32 330.73 332.05 333.53334.66 335.07 336.33 337.39 337.65 337.57336.25 334.39 332.44 332.25 333.59 334.76335.89 336.44 337.63 338.54 339.06 338.95337.41 335.71 333.68 333.69 335.05 336.53337.81 338.16 339.88 340.57 341.19 340.87339.25 337.19 335.49 336.63 337.74 338.36;run;proc gplot;plot y*time;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 1The ARIMA ProcedureWARNING: 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 = yMean of Working Series 334.5044Standard Deviation 3.151627Number of Observations 72AutocorrelationsLag Covariance Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1 Std Error0 9.932752 1.00000 | |********************| 01 9.014050 0.90751 | . |****************** | 0.1178512 7.168604 0.72171 | . |************** | 0.1917443 5.090716 0.51252 | . |********** | 0.2263504 3.474700 0.34982 | . |******* . | 0.2419325 2.452361 0.24690 | . |***** . | 0.2488586 2.017285 0.20309 | . |**** . | 0.2522377 2.087944 0.21021 | . |**** . | 0.2544988 2.625108 0.26429 | . |***** . | 0.2568989 3.618821 0.36433 | . |******* . | 0.26064710 4.814571 0.48472 | . |**********. | 0.26762711 5.806306 0.58456 | . |************ | 0.27955412 5.979308 0.60198 | . |************ | 0.29604513 5.149264 0.51841 | . |********** . | 0.31258414 3.660844 0.36856 | . |******* . | 0.32430515 2.053220 0.20671 | . |**** . | 0.33007216 0.808334 0.08138 | . |** . | 0.33186517 0.013455 0.00135 | . | . | 0.33214218 -0.322607 -.03248 | . *| . | 0.33214219 -0.269167 -.02710 | . *| . | 0.33218620 0.111604 0.01124 | . | . | 0.33221721 0.821916 0.08275 | . |** . | 0.33222222 1.689632 0.17011 | . |*** . | 0.33250823 2.415631 0.24320 | . |***** . | 0.33371524 2.508248 0.25252 | . |***** . | 0.336167"." marks two standard errors（3）The SAS System 10：20 Tuesday, September 20, 2013 2The ARIMA ProcedureInverse AutocorrelationsLag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 11 -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 AutocorrelationsLag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 11 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 3The ARIMA ProcedurePartial AutocorrelationsLag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 119 0.00630 | . | . |20 -0.08454 | . **| . |21 0.06247 | . |* . |22 0.01467 | . | . |23 0.02958 | . |* . |24 -0.10605 | . **|. | Autocorrelation Check for White NoiseTo Chi- Pr >Lag Square DF ChiSq --------------------Autocorrelations--------------------6 139.50 6 <.0001 0.908 0.722 0.513 0.350 0.247 0.20312 242.38 12 <.0001 0.210 0.264 0.364 0.485 0.585 0.60218 283.85 18 <.0001 0.518 0.369 0.207 0.081 0.001 -0.03224 301.25 24 <.0001 -0.027 0.011 0.083 0.170 0.243 0.253解释：从自相关图中可以看出：这是一种递增趋势的非平稳数列。

3.解（1）The SAS System 07:00 saturday, September 21, 2013 3 The ARIMA ProcedureWARNING: 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 = yMean of Working Series 98.12361Standard Deviation 48.93531Number of Observations 72AutocorrelationsLag Covariance Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1 Std Error0 2394.665 1.00000 | |********************| 01 124.786 0.05211 | . |* . | 0.1178512 -114.286 -.04773 | . *| . | 0.1181713 -258.859 -.10810 | . **| . | 0.1184384 -522.593 -.21823 | .****| . | 0.1198015 -301.208 -.12578 | . ***| . | 0.1252006 -143.875 -.06008 | . *| . | 0.1269437 69.899041 0.02919 | . |* . | 0.1273388 -179.450 -.07494 | . *| . | 0.1274309 -59.394302 -.02480 | . | . | 0.12804110 84.477704 0.03528 | . |* . | 0.12810811 387.659 0.16188 | . |*** . | 0.12824312 642.829 0.26844 | . |***** | 0.13105013 -29.665211 -.01239 | . | . | 0.13847714 95.360212 0.03982 | . |* . | 0.13849215 -380.392 -.15885 | . ***| . | 0.13865116 -356.928 -.14905 | . ***| . | 0.14115617 -461.638 -.19278 | . ****| . | 0.14332518 163.505 0.06828 | . |* . | 0.14688319 -150.758 -.06296 | . *| . | 0.14732320 222.713 0.09300 | . |** . | 0.14769621 396.093 0.16541 | . |*** . | 0.14850722 -166.053 -.06934 | . *| . | 0.15104423 307.584 0.12845 | . |*** . | 0.15148624 125.349 0.05234 | . |* . | 0.152991"." marks two standard errors判断：由该序列的时序图可知周期性的平稳数列（3）The SAS System 07:00 saturday, September 21, 2013 3The ARIMA ProcedureInverse AutocorrelationsLag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 11 0.01388 | . | . |2 0.18092 | . |****. |3 0.07951 | . |** . |4 0.19614 | . |****. |5 0.08263 | . |** . |6 0.06712 | . |* . |7 -0.04348 | . *| . |8 0.03328 | . |* . |9 0.10540 | . |** . |10 -0.04671 | . *| . |11 -0.03890 | . *| . |12 -0.17068 | . ***| . |13 0.10690 | . |** . |14 -0.05577 | . *| . |15 0.06454 | . |* . |16 -0.02009 | . | . |17 0.12211 | . |** . |18 -0.06930 | . *| . |19 0.09803 | . |** . |20 -0.06746 | . *| . |21 -0.07246 | . *| . |22 0.08905 | . |** . |23 -0.04974 | . *| . |24 -0.01327 | . | . |Partial AutocorrelationsLag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 11 0.05211 | . |* . |2 -0.05058 | . *| . |3 -0.10339 | . **| . |4 -0.21302 | .****| . |5 -0.12668 | . ***| . |6 -0.09591 | . **| . |7 -0.03342 | . *| . |8 -0.17421 | . ***| . |9 -0.11437 | . **| . |10 -0.05064 | . *| . |11 0.10671 | . |** . |12 0.22503 | . |***** |13 -0.03372 | . *| . |14 0.09978 | . |** . |15 -0.04635 | . *| . |16 -0.00085 | . | . |17 -0.16166 | . ***| . |18 0.08878 | . |** . |The SAS System 07:00 saturday, September 21, 2013 3The ARIMA ProcedurePartial AutocorrelationsLag Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 119 -0.15989 | . ***| . |20 0.09844 | . |** . |21 0.07272 | . |* . |22 -0.11805 | . **| . |23 0.06340 | . |* . |24 0.01689 | . | . |Autocorrelation Check for White NoiseTo Chi- Pr >Lag Square DF ChiSq --------------------Autocorrelations--------------------6 6.56 6 0.3634 0.052 -0.048 -0.108 -0.218 -0.126 -0.06012 15.94 12 0.1938 0.029 -0.075 -0.025 0.035 0.162 0.26818 24.64 18 0.1352 -0.012 0.040 -0.159 -0.149 -0.193 0.06824 31.39 24 0.1428 -0.063 0.093 0.165 -0.069 0.128 0.052判断：由于p的值都大于a，，所以该序列为纯随机序列。