多元时间序列建模分析

1、应用时间序列分析实验报告实验名称多元时间序列建模分析姓名学号班级实验地点实验日期指导教师实验目的：1、熟悉单位根检验；2、掌握ARIMAX模型建模涉及实验的相关情况介绍（包含使用软件或实验设备等情况）：SAS、excel表格、word。实验内容:1、我国1950-2008年进出口总额数据（单位：亿元）如表6-15所示表6-15年份出口总额进口总额19502021.3195124.235.3195227.137.5195334.846.119544044.7195548.761.1195655.753195754.55019586761.7195978.171.2196063.365.11961

2、47.743196247.133.819635035.7196455.442.1196563.155.319666661.1196758.853.4196857.650.9196959.847.2197056.856.1197168.552.4197282.9641973116.9103.61974139.4152.81975143147.41976134.8129.31977139.7132.81978167.6187.41979211.7242.91980271.2298.81981367.6367.71982413.8357.51983438.3421.81984580.5620.519

3、85808.91257.819861082.11498.3198714701614.219881766.72055.1198919562199.919902985.82574.319913827.13398.719924676.34443.319935284.85986.2199410421.89960.1199512451.811048.1199612576.411557.4199715160.711806.5199815223.611626.1199916159.813736.5200020634.418638.8200122024.420159.2200226947.924430.320

4、0336287.934195.6200449103.346435.8200562648.154273.7200677594.663376.9200793455.673284.62008100394.979526.5(1)使用单位根检验，分别考察进口总额和出口总额序列的平稳。(2)分别对进口总额序列和出口总额数据拟合模型。(3)考察这两个序列是否具有协整关系。(4)如果这两个序列具有协整关系，请建立适当模型拟合它们之间的相关关系(5)构造该协整模型的误差修正模型。实验过程记录（含程序、数据记录及分析和实验结果等）：时序图如下：单位根检验输出结果如下:序列x的单位根检验结果：AuKHHmtedDi

5、ckey-FullerUnitHootTestsT/peLm宅看RgPr<RhoTauPr<TauFPr>FZ&roMe&n0-42.7C3I<.0001-5.2S<.00011-25.55210.0001-8.0®0.0024Sincl&Mbm0-49.74860,0003-6.83<-000117.02o.oaio1-S4.G6B20.0005-8*M0.09686.700.0010Trend0-67.67370400037.25<.000128.81O.OUIO1-66.52150.0003r6l5U.00031

6、3.370.0010序列y的单位根检验结果LamentedDickey-FullerUhitRootTestsFypeLacsRMPr<RholauPr<TftuFPr>FZeroMean0-22.03S30.0006-24430.015210.7440(MS0脑脑SingleMean0-23.36770.0008-Z.880.04084J1.I：I5I：IO1-3.33340,550?-0.440.8371U.530J266Trend0-47.87210.0003-4.440.0032IC.530.00101-22.66540.0265-1.850.67032.950.59

7、30序列y和序列x之间的相关图如下:CrosscorrelationsLigCovariinceCorre1at!or7987S54921O1234587891-22-1903639-.00739.«-21798418。，叩309.-20104760750.0i1057.-193E473700,01413.-IS87034即0,03371.聘.-17255lSfi450.09091.同.-I668908720.03443.-I51I4415S950.05505.*.-1434R345550.,13414.*.05300.籽.-12194298820.0752

8、5.榭.-111478830J864S.件怖本-102UDS9S9Sf-02705.触.-8Z65539670.11061.恻.-0用MW4叼0,25111.11riill1ali1l|llIIIBPLI11!"-1276015760JDS30.HOK.-6471S0587(M必7.树*-511的51。陶0.42972.HrQiahill!)illallijliQiruiT>,Ti-T1"T1'T11TB残差序列自相关图：AutocorrelationPlotofResiduaIsLagCovarianceCornelatIon-1s37654321I1234

9、5678S*1StdError0249038S54L00O00邙斗下而中不下而邛邛沏厚*n=n*面面mm而单01-9554463-.oaess.«90.10B60D2-43069086-J72900.10675731790412190,69467山山山山*XBL*1Jj*JjML1I'X*W1|ll|ll|1f|l1|ll|Blf花,E.戏、f|0.109SS3413357750.005361a0.1618025-J9651657-J1904.郴0.15180461062657630.426600.152S61119684970,007860.1BGB988-167S5115

10、-.06742*瑞0.165843g657367420.263900.1B6155ID-2862000-.011430.170851111-11436601-,04591,第0.17DBG012470B64120,18903擀懒.0.17100013-mmi-.012990.17335014-8251250-.03312儒0.17336915337639560J3554卅栅.0.17344116-2367324-.008510.1746411?-76399B2-403087小0.174647182232884G0.092040.17470819-42BG70-,001720.1752592D-

11、5042224-.020240.17525821114511520,04597».0.17528522-24126B-.000970.175422加&rkatwostandarderrors自相关图显示。延迟6阶之后自相关系数都在2倍标准差范围之内，可以认为残差序列平稳。对残差序列进行2阶自相关单位根检验，检验结果显示残差序列显著平稳，如下图:残差序列单位根检验结果：AugmentedDickey-FijiIerUnitRootTestsTypeLagsRhoPr<RhoTauPr<TauFPr>FZeroMeanQ-Z2.08S90.0006-2.43UJ

12、15210.4236OJ840LOS0.6903SingleMean0-28,66770.0008-2,980.04004,710.05001-3.83340.5502-0.440.89710,580,3268Trend0-47.67210.0003-4.440.003210.530.0010,-22用540,0235-kS50.B7092.950.6830残差序列平稳，说明序列Y与序列X之间具有协整关系，我可以大胆的在这两个序列之间建立回归模型而不必担心虚假回归问题。考察残差序列白噪声检验结果，如下图：残差序列白噪声检验结果：TheARIMAProcedureAutocorrelationC

13、heckofResidua.lsToLagChiSquareDFPr>11oymuiluuuvrcialiuiis666.766<.0001-0.036'0J730.6950.005'0.1190.4271278.1712<100010.000-0,0670.2S4-0.011-0.0460.189ie81.3818<.0001-0.013-0.033ij.|jb-04010'0.0310.09224SL6924<.0001-0.002-0,0200.046-0.001-0.0100.004输出结果显示，延迟各阶LB统计量的P值都大于显著水

14、平0.05,可以认为残差序列为白噪声检验结果，结束分析。出口序列拟合的模型为：lnxtARIMA(1,1,0),具体口径为：Vlnxt=0.14689+1辫1-0.38845B进口序列拟合的模型为lnytARIMA(1,1,0),具体口径为:一.1'、lnyt=0.146722t1-0.36364lnyt和lnxt具有协整关系。协整模型为：lnyt=0.991791nxi；t-0.69938；t误差修正模型为："lnyt=0.9786、lnxt-0.22395ECMtSAS?序如下：dataexample6_4;inputxy;t=_n_；cards;195020.021.3

15、195124.235.3195227.137.5195334.846.1195440.044.7195548.761.1195655.753.0195754.550.0195867.061.7195978.171.2196063.365.1196147.743.0196247.133.8196350.035.7196455.442.1196563.155.3196666.061.1196758.853.4196857.650.9196959.847.2197056.856.1197168.552.4197282.964.01973116.9103.61974139.4152.81975143.

16、0147.41976134.8129.31977139.7132.81978167.6187.41979211.7242.91980271.2298.81981367.6367.71982413.8357.51983438.3421.81984580.5620.51985808.91257.819861082.11498.319871470.01614.219881766.72055.119891956.02199.919902985.82574.319913827.13398.719924676.34443.319935284.85986.2199410421.89960.119951245

17、1.811048.1199612576.411557.4199715160.711806.5199815223.611626.1199916159.813736.5200020634.418638.8200122024.420159.2200226947.924430.3200336287.934195.6200449103.346435.8200562648.154273.7200677594.663376.9200793455.673284.62008100394.979526.5run;procgplot;plotx*t=1y*t=2/overlay;symbol1c=blacki=joinv=none;symbol2c=redi=joinv=nonew=2l=2;run;procarimadata=example6_4;identifidentif/var=xstationarity/var=ystationarity=(adf=1);=(adf=1)