农村居民家庭人均纯收入影响因素分析

农村居民家庭人均纯收入影响因素分析《农村居民家庭人均纯收入影响因素分析》班级：商学院姓名：学号：指导教师：完成时间：年月曰农村居民家庭人均纯收入影响因素分析摘要：随着我国工业化与城市化建设的发展，农村问题越来越凸显，留守问题、看病问题、养老问题等，农民收入问题亦是国家各界人士十分关注的问题。本文旨在用计量经济学方法简单分析农村居民家庭人均纯收入的影响因素。关键字：农村居民家庭人均纯收入财政年度支农支出农业机械总动力农作物播种总面积乡村就业人数乡村人口数第一产业总产值正文:一、引言国家“十二五”规划第六章拓宽农民增收渠道中明确提出：加大引导和扶持力度，提高农民职业技能和创收能力，千方百计拓宽农民增收渠道，促进农民收入持续较快增长。同时“十二五”规划中明确提出以下几点：1、稳定粮食播种面积、优化品种结构、提高单产和品质。2、健全农业补贴制度，坚持对种粮农民实行直接补贴，继续实行良种补贴和农机具购置补贴，完善农资综合补贴动态调整机制。3、推进农业技术集成化、劳动过程机械化、生产经营信息化。结合这几方面，本文从第一产业总产值、财政年度支农支出、农业机械总动力等几个方面分析其对农村居民家庭人均纯收入的影响。二、预设模型令农村居民家庭人均纯收入丫（元）为被解释变量，农作物播种总面积X1（千公顷）、乡村就业人数X2（万人）、乡村人口数X3（万人）、第一产业总产值X4（亿元）、财政年度支农支出X5（亿元）农业机械总动力X6（万千瓦）为解释变量，据此建立回归模型。三、数据搜集从中国统计年鉴得到如下数据：年度农村居农作物乡村就乡村人第一产财政年农业机民家庭播种总业人数口数X3业总产度支农械总动人均纯面积X1X2（万（万人）值X4支出X5力收入Y（千公人）（亿元）（亿元）X6（万千（元）顷）瓦）1990686.3148362.47708841385062221.7628707.7

1991708.63149585.848026846205342.2243.5529388.61992784149007.48291849965866.6269.0430308.411993921.6147740.48546853446963.76323.4231816.67319941221148240.48802856819572.69399.733802.56519951577.7149879.490258594712135.8430.2236118.031519961926.1152380.490288508514015.3510.0738546.96919972090.1153969.490398417714441.8560.7742015.62919982162155705.490218315314817.6626.0245207.773119992210.3156372.489828203814770.0677.4648996.183220002253.4156299.489348083714944.7766.8952573.682120012366.4155707.486747956315781.2917.9655172.19720022475.6154635.481217824116537.01102.757929.852520032622.2152415475067685117381.71134.8660386.52420042936.4153552.469717570521412.71693.7964027.953120053254.9155487.4625874544224201792.468397.875200635871521494534673160240402161.3572522.120074140.4153463.4436871496286273404.776589.6920084760.6156265.4346170399337024544.0182190.4720095153.2158613.4250668938352266720.4187496.1520105919160674.8414186711340533.68129.5892410.4四、建立模型1、散点图分析2、单因素或多变量间关系分析□Group:UNTTTLEDW&rkfile:UlSTITTLECXUntitledIdIB1翌CoirreliitionMatrix¥X1X2X3X4X5X6YI.OODDOOO.80D9S7-D.SSIS91-D.55D233D.99B239D.923B20D.579356X109009371000000-3520B63-3.6823273.793056。.&95B661772623X2■A.884B91■0物6631.0000003.918402-3.891367-3.&SDB63-3.8&4D52K3」。550238刈68232709184021ooooon盘M5376-3682791-&981893X4099B239O7S3B56■0.391367-3.H537610000001&274SS0.972924K50523B200695B66-096DB63-DS327^1D笠斑81OODDOODB71277XS0979GS60.772B23-0864052-3.991D933.&T2&2418712771.000000三———■mr_J■由散点图分析和变量间关系分析可以看出被解释变量农村居民家庭人均纯收入丫（元）与解释变量农作物播种总面积X1、乡村就业人数X2、乡村人口数X3、第一产业总产值X4、财政年度支农支出X5、农业机械总动力X6呈线性关系，因此该回归模型设为：Y"o+BX+B2X2+B3X3+B4X4+85X5+B6逸+u3、模型预模拟用Eviews做OLS回归分析得：

¥1£：照|斤0€：|口切已疽|Print]lMam£|FreiEze|EsumatelForeostl5tats|Reslds|DependentVariable:YMethod:LeastSquaresDate:06/03/12Time:21:58Sample:19902010Includedobservations:20vanabeCoefncientStd.Errort-StatisticProb.CX1K2X3蹈X5X6CX1K2X3蹈X5X6-6067.3551991.952-3.0459340.00940.0202910.01U631.7701310.1001-00803230.066175-1.2213490.24360.0716520.04394-116306570.126900936500.015061&.215632000000.0029620.0430460.0688200.94620.0367970.0153282.4006210.0320R-squared0.998843AdjustedR-squared0.998308S.Eofregression60.94868Sumsquaredrssid4S29164Loglikelihood-10B.271&Durbin-Watsonstat2.060182Meandependentvar2626.790S.D.dependentvarM81.920Akaikeinfocriteri&r11.32716Schwarzcriterion1167566F-statistic18&9.907Prob(F-statistic)0.000000Y=・6067.355+0.02029X1・0.08082X2+0.07165X3+0.09355X4+0.002962X5+0.03680X6(-3.04593)(1.77013)(-1.2213)(1.6307)(6.2155)(0.06882)(2.4006)RA2=0.9988*人2=0.9983F=1869.907D.W.=2.0602五、模型检验1、计量经济学意义检验(1)多重共线性检验与解决求相关矩阵得到：□Group:UhlTTTLEDWorkfiIm:UMTTTl£D\Untitled亘亘Epi]机匚「Object]PrhtF占mejF「壬nSampleSheet5iats|5pec||rcorrelationMatrixYXIX2X3XIX5X6Y1003000■1003997994091-3.9bD2393.99023935230203.&753SG*X1O.80D9871.00DDOO-O.52BB63-D.682327D.783B56D.695666D.772B23X29904091-3S2BB6310030003.910402-3.991367-3.55DB63-3.064052X3-0.95D238-0.BB23270.91B4021.QODDOO-D.945376-D.882791-D.981B93X4-0.990239O7B30669891367-3.9453701.0030003.92M翩3.S72&24一X50.523B200.655B66-Q.95D363-D.332791D.527488I.OODDOO0.871277XS0379356"1772623刈364052-D991093"712771oonnooT4LnirJ卜发现模型存在多重共线性。接下来运用逐步回归法对模型进行修正:将各个解释变量分别加入模型，进行一元回归：作Y与X1的回归，结果如下：□Equation:UNTTLEDWorkfile:UNTTLED\Untitl&d|=■||回||£3|Pr°dciBjm吐|PrintNameFi•至况|£酣吊乖|山心渤StstsResidsDependentVariable:YMethod:LeastSquares□ate:06/03/12Time:22:16Sample:19902010Includedobservations:21VariableCoefficientStJ.Errort-StatisticProb.C-40679.OG8FU.810-5.6038020.0000X10.3334620.06S7175.90494-3o.oaooR-squared0.&S3408Meandependentvar2559,&48AdjustedR.-squared0,635167S.D.dependentvar147C.614SE.ofre-gression89189EDAkaiikeinfocriterion16一51497Sumsquaredresi-d15114052Schwarzcriterion1S.61445Loglikelihood-1714072F-statist\g35.81354□urbin-Watso-nstat0.319004Prob(F-5tatistic)0.000009作Y与X2的回归，结果如下:圳日*||Prodcibjmtt|V「int|MmmiEwtjrriate|Foi•业如t|StatsRe印成|DependentVariable:YMethod:LeastSquares□ate:06/03/12Time:22:17Sample:19902010Includedobservations:21VariableCoefficientStd.Errort-Statisti-cProb.C28798.133124.S7&J.2157680.0000X2-0.55BS520.056206-8.40&6580.0000R-squa.red0.780112Meandependentvar2559娜AdjustedR.-squared0.776961S.D.dependentvar1476.614S.E.of「egression697.3612Akaikeinfiocriterion16.02288Sumsquaredresid9239941.Schwarzcriterion16.12236Loglikelihood-166.2402F-statistic70.67023Durbin-Watsonstat0.1000G1Prot)(F-5tatistic)o.oaoooo作乂与品的回归，结果如下:QEquation;EQ13W&rkFile;UNTITLE!>\Untitled|Mir^~||M|Proc|Db^ject|Print|NameFrEeze|EstimatsForeca5t|£tBts|Residn|DependentVariable:YMethod:LeastSquaresDate:0EJ03/12Time:22:17S-ample:19902010IncludedobserYations:20VariableCoefficientStd.Errort-Statisti-cProb.C20673.S61398一56614.782180.0000X3-0.22S9740.017613-12.941240.0000R.-squared0.902952Meandependentvar2526J90AdjustedR-squared0.897561S.D.depencientvar1481.S20S.E.ofregressio-n4743063Akaikeinfiocriterion15.26B22Sumsquaredresid4049379.Schwarzcriterion15.35579Loglikelihood-1505622F-statistic167.4757Durbin-Wats-onstatO.16460SProb(F-statisti-c)0.000000作丫与乂4的回归，结果如下:Print|Name|曰军距|Estim自tie|F口「ecast|51日ts||DependentVariable:¥Method:LeastSquaresDate:OB/03/12Time:22:17Sample;19902010Include-dobsefyations;21VariableCoefficientStd.Errort-Statisti-cPTob.C-63.0098842.22730-1.4921600.1521X401474330.00208170.838980.0000R-squared0.99622SMeandependentvar2659.848A-djustedR-squared0.996030S.D.dependentvar1476.614S.E.ofregression93.04413Akaikeinftcriterion11.99442Sumsquaredresid164487.0Schwarzcriterion12.09390Loglikelihood-123.9414F-statistic5018.1B1Durbin-Wats-onstat1.241548Prob(F-statiStic}0.000000作乂与乂5的回归，结果如下:View|F^rcxz|ObjectPrintlNarneFreezeEstiirateForecastStats|Rjesids□epen-dentVariable:YMethod:LeastSquares□ate:06/03/12Time:22:18Sample:19902010Includedobservations:21VariableCoefficientSt-d.Errort-StatisticProb.C1475.635163.15909.0441510.0000X50.621砌005906610.,523270.0000R-square<i0.853552Meandepen<ient\rar2559.848AdjustedR-squared0845844S.D..dependentvarU76.&14S.E.ofregression5757576Akaikeinfiocriterion15.65349Sumsquaredresid6386258.Schwarzcriterion15.75297Lo-glikelihood-162.3&16F-statistic110.7391□urbin-Watsonstat0.216775Prob(F-statiStic)0.000000作乂与*6的回归，结果如下:

View]Pr&c|Dbject|Print|NameIFreeze]E§tinia~be|ForcesstIStatsIResidsIDependentVariable:YMlethcxj：LeastSquares□ate:D&/03/12Time:22:18S-ample:19902010Includedobservations:21VariableCoefficientS-td.Errort-StatiSticProb.C13197181918911-B.87743S0.0000X60.0718060.00333721.5155B0.0000R-squaredQ.9网574Meandependentvar2559.843AdjustedR-squared0.958499S.D.dependent切1476.E14S.E.ofregressio-n300.8115Akaikeinfocriterion14.34124Sumsquaredresid1719M4Schwarzcriterion14.44072Loglikeliho-txi-148.5830F-statistic462.9200Durbin-Wats-orstat0.305528Prob(F-statiStic)0.000000依据可决系数最大的原则选取X6作为进入回归模型的第一个解释变量，再依次将其余变量分别代入回归得:作乂与X6、X1的回归，结果如下:OEquation;UNTTTLEDWorkfile-;^^4\Untitle-d口回切丘呼|Proc|DbjEctlPrint|Name|FreezeEstiEmte|Fer£ca5iz|£ta~ts|ResidslDependentVariable:YMethod:LeastSquaresDate:06/09/12Time:12:5®Sample:19902010Include-dobseivati&ns:21VariableCoefficientStd.Errort-StatisticProb.C-73297664329.490LW29BG0.1077XE0066054000526812.538360.0000X10.0412160029&631.3894670.1E16R-squared0.964353Mean-dependentvar2559.848A-djustedR-squared0.960437S.D.dependentvar1476.614S.E.ofregression2937047Akaikeinfocriterion14.33459Sumsquaredresid1552724.Schwarzcriterion14.48381Loglike!hood-147.5132F-statistic243.7622Durbin-Watsonstat0.298684Prob(F-statiStic)0.000000作乂与X6、X2的回归，结果如下:

View|Proc|ObgectIPrintNameFrEEje|Es~tiEmtE|Forcczact|EtBtBREsidg|DependentVariable:YMethod:LeastSquares□ate:06/09/12Time:12:51Sample:19902010Includedobservations:21VariableCoefficientStd.Erro-rt-StatisticProb.C3733.59528&5.54&1.3029270.2090x&0.0620750.0063519.7734100.0000X2-0.0360370.054349-1.7B70410.0942R-squareJ0.966402Meandependentvar2S59.B4SAdjustedR-square^i0.962&69S.,D.dependentvar1476.614S.Eofregression285.2984Akaikeinfocriterion14.27G51Sumsquaredresid146S113.Schwarz-criterion1442573Loglikelihood-146.9034F-statistic258.8769Durbin-WatsonsLat0.313811Prob[F'-statistic)0.000000作丫与X6、X3的回归，结果如下：QEquaticn;UNTTTLEDWcrkfile-;咪咪4'Untitled|u||目||S3]Miew|防。匚|《切日国Print|Name|Freeze|EstirnatE；|ForecEtats|Resids|DependentVariable:YMethod:LeastSquares□ate:D6J09/12Time:12:52S-ample:19902010Includedobservations!20VariableCoefficientStd.Errort-StatisticProb.C-8630.824567土9721.51盟190.1470X&0.09505900181965.2240610.0001X30.0764500.吒痢1一2839450.2164R-squared0.962750Mean-dependentvar2626790AdjustedR-squared0.958368S.D.dependentvar1481.920S.E.ofregressio-n302.3691Akaikeinfocriterion14.39866Sumsquaredresid1554-260..Schwarzcriterio-n14.54801Loglikeliho-cxi-140.9866F-statistic219.6908Durbin-Watsonstat0.329402Prob(F-statistic)0.000000作丫与X6、X4的回归，结果如下:

切已w|Proc|Dfaj已ct|Print|Name|FreezeEstiEate|Fermcawi:|£ta~ts|R^sjdg|DependentVariable:YMethod:LeastSquaresDate:06/09/12Time:12:53Sample:19902010Includedobservations:21VariableCoefficientSid.Errort-StatisticProb.C-253.S89577.37296-3.7983500.0013X60.0120740.003&313.3253230.0038X40.1237420.00732116.903250.0000R-squared0.997663Mean<iependentvar2559845AdjustedR-squared0.997404S.D.dependentvar1476.E14S.E.ofregression76.23765Akaikeinfocriterion11.51074Sumsquaredresid101892.4Schwarzcriterion11.7599ELoglikelihcxxj-11S.9128F-statistic3842807Durbin-Watscnstat12K2559Prob(F-statiSticJ0.000000作丫与X6、X5的回归，结果如下:Equation:UNTITLEDWorldile:咪咪4\Untitledi=i回饥Proc|DbjectlPrint|NameIFreeze|Ertiniat&|Forecagt|£tBts|ResidrlDependentVariable:YMethod:LeastSquaresDate:06/09/12Time:12:53Sample:19902010Includedabservations:21VariableCoefficientStd.Errort-StatisticProb.C-6607700205.6408-3.2132250.0048X600633250.00490210.877170.0000X50.1946620.0450194.32402G0.0004R-squared0.980662Meandependentvar2559.848AdjustedR-squared0.978513S.D.dependentrar1476.614S.E.ofregression216.4486Akaikeinfiocriterion13.72415Sumsquaredresid843299.9Schwarzcriterion13.87336Loglikelihocci-141.1035F-statistic颂一3978Durbin-Watsonstat0.450642Prob[F-statistk)0.000000在满足经济意义和可决系数的条件下选取X4作为进入模型的第二个解释变量，再次进行回归则：作乂与X6、X4、X1的回归，结果如下CJEquation:UNTITLEDWorkfle■:咪咪d'lJrititlmd1=1回口切此日PrintName|Freeze|Ewtimm怔|FeecbeUEtatg||DependentVariable:YMethcxi:LeastSquaresDate:06/09/12Time:12:57Samiple:19902010Includedobservations:21VariableCoefficientStd.Eirort-StatisticProb.C3209711931.8397-3.4444880.0031X60.0108870.0029983.6316500.0021X40.1204750.00608619.798020.0000X10.019S110.00&3163.136362O.OOEOR-squared0998570Mean-dependentvar2559848A-djustedR-squared0.998269S.D.dependentv^r1476614S.E.ofregression6161775Akaikeinfocriterion11.24942Sumsquaredresid64544.B9Schwarzcriterion11.44B38Loglilkelihood-114.1189F-statiStic3822.B54Durbin-Watsonstat2.089910Prob(F-statiSticJ0.000000作丫与X6、X4、X2的回归，结果如下□Equation;UNTITLEDW&rkfile;咪咪％Jntitled1=1回S3Vi巳冏|Pr口匚gbjE匚t|PrintNameF「匚去司E吕He日tE|f口「m匚日wtStatsResids□epentlentVariable:¥Method:LeastSquares□ate:06/09/12Time:12:58Sample:19902010Includedobservations:21VariableCoefficientStd.Errort-StatisticProb.C-981.7&68820.9376-1.1955220.24S2X60.0119720.0036633.2686370.0045X40.1257980.00822515415230.0000X20.0135540.0161040.8416630.4117R-squared0.997757Weandependentvar2559843AdjustedR-squared0997361S..D.dependentvar1476614S.E.ofregression76.85449Akaikeinfocriterion1166515Sumsquaredresid97B1635Schwarz-criterion1186411L&glikelihood-1184841F-statistic2620.605Durbin-Watsonstat13961S3Prob[F'-statistic}0.000000作丫与X6、X4、X3的回归，结果如下ViE仲|Proc|ObjectlPrint『日E"Fr能ze|EwtirnatEFwEcawtStatsREsjdd□epen-dentVariable:YMethod:LeastSquaresDate:06/09/12Time:12:58Sample:19902010Includedobservations:20VariableCoefficient.Std.Errort-StatisticProb.C-2274.5891416323-1.60605301278X60.0186350.0063292.9446580.0095X40.122670000734316.&94650.0000X3。一0208460.014-6831.4196740.1749R-squareJ09979T8Meandependentvar2626.790AdjustedR-squareri0997598S.D.dependentvsr1481.920S.E.ofregression72.S21&3Akaikeinfocriteri&n11.5862GSumsquaredresidS4382.41Schwarzcriterio-n11.78441L&glikelihood-111.852BF-statistic2631.907Durbin-Watsonstat1.5S1242ProbfF-statistic^0.000000作丫与X6、X4、X5的回归，结果如下□Equation:UNTITLEC•Warkfile-r咪咪4'UrititludViewIProc]DfagectlPrini:|NmEe|FrEeze|EstimateFear的口35七|£ta~ts|ResidsIDependentVariable:YMethod:LeastSquares□ate:06/0^/12Time:12:59Sample:19902010Includedobservations:21VariableCoefficientStd.Errort-StatisticProb.C-284-.8028T3.3ST4-3-3.5605960.0024X60.0130D600039753.2720030.0046X401189970.01055811.270430.0000X50.0U3040.0225700.6337630.5347R-squared0.997717Mean-dependentvar2559848AJjustedR-squaredD997315S.D.dependentvar1476.614S.E.ofregression76.62012Akaikeinfocriterion11.&8263Sumsquaredresid99540.68Schwarzcriterion11.8&158Loglikelihood-118一6676F-statisti-c2476.846Durbin-Watsonstat1170793Prob(F-statistic)0.000000在满足经济意义和可决系数的条件下选取X1作为进入模型的第三个解释变量，再次进行回归则：作乂与X4、X6、X1、X2的回归，结果如下Equation:UNTITLEDWorkfle-:味咪4\Unidtlmd1=1回Wew|田Print]PamE|Fi•匪回Egtirn3t&|Fo陡勇吐|Stats|Rmsjd』DependentVariable:YMethod:LeastSquares□ate:D&/09/12Time:13:01Sample:19902010Includedobservations:21A.VariableCoefficientStd.ErrorL-StatisticProb.——C-3063.274946.4814-3.2399090.0061XB0.0107400.00300B3.5724300.0026X40.1162170.00750715.480370.0000X10.0242150.0077843.110925O.OOE7X2-0.015&&10.01&121-0.9714320.3468R.-squared0.998602Meandependentvar2669.848AdjustedR-squared0.998263S.D.dependent盹r1476614S.E.ofregressio-n61.72007Akaikeinfocriterion11.28735Sumsquaredresid60949.87Schwarzcriterion11.53E05Loglikelih&od-113.5172F-statisti-c2867.E78Durbin-Wats-onstat2.132460Prob(F-statiStic)0.000000T-作丫与X4、X6、X1、X3的回归，结果如下Equation:UNTITLEDWorkfle-:联咪41Untitled1=1回酒ewIlVod口切巳匚Print小加£片「王正左|EstimatEi|ForecastEtatsDependentVariable:YMethod:LeastSquaresDate:06/09/12Time:13:02Sample:19902010Includedobseivati&ns:20VariableCoefficientStd.Errort-Sta.tisticProb.—-C-2973.97813Q8.2G3-2.2732270.0381X6000991400069051.4357980.1716X40.1212040005&2018.308850.0000X1001953G00080162.2160140.0426X3-0.0019270.01G&99011539B0.9097R-squared0.998476Meandependentvar2626.790A-djustedR-squared0.998070S.D.dependentvar1481.920S.E.ofregression55.10023Akaikeinfocriterion11.40205Sumsquaredresid63570.60Schwarzcriterion11.B6098Loglikelihtxxi-109.0205F-statistic2467.626Durbin-Watsonstat2.213093Prob(F-statiStic)0.000000T作乂与X4、X6、X1、X5的回归，结果如下□Equation:UNTITLEDWorkfik:咪咪*Untitl迎4叵伸|尸「口＜:|口bj已匚PrintNaE^|FrEEze|EMeate|%「旦由5土|Stats|Res-id§|DependentVariable:YJLMethod:LeastSquaresDate:06J09/12Time:13:03Sample:19902010Includedobseivations:21VariableCoefficientStd.Errort-StatisticProb.C-332&.310926,4023-3.5905680.0024X60.0122280.00317.33S536520.0014X40113234000&58513.1898S0.0000X10.0206950.0052893.2905210.0046X500213890.0180941.1821050.2544R-squared0.998639Mean<iependentvar2559.B48A-djustedR-squared0.998298S.D.dependentvar1476.E14S.E.ofregression60.90998Akaikeinfocriterion11.26093Sumsquaredresid59360.41Schwarzcriterion11.50962Loglikelihood-113.2397F-statistic2934.508Durbin-Watsonstat2.0E0097Prob(F-statiStic)0.000000可见加入其余任何一个变量都会导致系数符号与经济意义不符,故最终修正后的回归模型为：Y=-3209.71+0・01089X6+0・1205X4+0・01981X1(-3.4449)(3.6317)(19.7980)(3.1364)RA2=0.9985*人2=0.9983F=3822.85D.W.=2.0899(2)异方差检验图示法eA2与X6的散点图如下：

说明e人2与X6不存在异方差性。e"2与X4的散点图分析说明e"2与X4不存在异方差性。e"2与X1的散点图分析□Graph:UNTITLEDWorkfile:I^O\Untitled[u||回|S3]傩讪Objgct|PrintlNmrndAddTEKt|LiriE/iShatJe|只门[110丁己|~TErnphtE|Z&orn|32000-1280002400020000窟16000120006000-。4000-oooQ_144-0001520001600001&4-000X1说明e"2与X1不存在异方差性。②G-Q检验对20组数据剔除中间五组剩下的进行分组后第一组(1990-1997)数据的分析结果：Ptoc|PbrjEct|PrintNameFreeze!E5tjmm~be|Foreca吐|泌国只应时DependentVariable:YMethod:LeastSquares□ate:0&/09/12Time:13:10S-ample:1990199-7Includedobservations:8VariableCoefficientStd.Errort-StatisticProb.C■4636.2671143.983-4.0527400.0154X60.0274240.01125324369930.0714X40.0980290.0118258.2900080.0012X10.0270070.00SG943.1065740.0360R.-squared0598867Meandependentvar1239425AdjustedR-squared0.598017S.D.dependentvar561.3869S.E.ofregression24.99686Akaikeinfocriterion3.582230Sumsquaredresid2499.372Schwarzcriterion9.621951Loglikeliho-oKi-34.32892F-statistic1176.542□urbin-Wats-onstat2.286976Prob(F-statisti-c)0.000002残差平方和RSS1=2499.372第二组(2003-2010)数据的回归结果:^3Equation:UNTITLEDWorkfile:味昧4[lJnidtludVi丘山|P「oi:|cE丘吐|PrintName|Freeze|EstirnateForecastStatsDependentVariable:YMethod:LeastSquares□ate:06/05/12Time:13:11Sample:20032010Includedobsenrations:8VariableCoefficientStd.Errort-StatisticProb.C-3473.2863251.539-1.0552160.3508X60.0490560.0197752.4807480.0682X40.0715910.0300352.3835400.0757X10.0117010.0211900.5521810.6102R.-squared0.997113Meandependentvar4046.713AdjustedR-squared0..994947S.D.dependentrar1155640S.E.ofregression82.14658Akaikeinfocriterio-n11.96174Sumsquaredresid26992.25Schwarzcriterion12.00U6Loglikeliho-OKi-43.84696F-statisti-c460.4546Durbin-Wats-onstat1.770331Pr&b[F-statistic)0.000016残差平方和RSS2=26992.25所以F=RSS2/RSS1=26992.25/2499.372=10.799613a=5%下，临界值F0.05(4,4)=15.98>F因此不拒绝两组子样方差相同的假设，从而该总体随机项不存在异方差性。③White检验Equation:EQ1111Workfle:味味的Untitled1=1回Pr凸匚|口定庭吐|PrintNarnsFreezeEstinristeIForecastStstsR&sidsWhiteHeteroskedastkityTestF-statistic1.001808Probability0.461819Qbs*R-squared6.307971Probability0.389590TestEquation:DependentVariable:RESIDEMethod:LeastSquares□ate:06/09/12Time:13:12Sample:19302010Includedobservations:21VariableCoefficientSLd.Errort-StatisticProb.C-730617.8S44273&.-0.1342190.8961X53.1330362.&119201.1995150.2502瀛岖-291E052.52E-0611539520.2678X4-1.5983B52.&11862-0.6119640.5504X4^2G67E057.55E-060.8719300.3980X110.3664271.026900.U59610.8860X1*2-J.94E-050.00023101705860.8670R-squared0.300380Meandependentvar3073.557AdjustedR-squared0.000542S.D.dependentvar6592145S.E.ofregression6590.358Akaikeinfocriterion20.68580Sumsquare-dresidE.08E408Schwarz-criterion21.03398L&glikelihood-210.2009F-statistic1.001S08Durbin-Watso-nstat2.107550Prob[F-statistic)0.461S19LM=nRA2=21*0.30038=6.30798取a=5%，自由度为6z2分布的临界值为12.59LM<z2接受原假设即不存在异方差P=0.389590>0.05的显著性水平，所以不存在异方差(3)序列相关性检验①从残差e与e(-1)及e与时间t的关系图(如下)看，随机项不序列相关。7—一・—O-Graph;GRAPH06Workfle;I^^^Untitled|口||回||S3]Me讪Pror]Ohject|Print]Nhee|4ddT已k±|Lin的/Sbiadt|Rjseo¥e|呼late]Option^Eoofn|〔DOoTOC\o"1-5"\h\zI50-七口I□o0a-口M口o0□0

0LU-M-□Q-wa--150-□-20D11111-200-150-100-50050100E(-1)如图所示，不存在序列相关D.W检验r^3Equation:EQ1111Workfile:Untitled1=1回曜"P「ciiz|ci|jjEd:|P「int|MmrriE|F「既花|E5tirnatE|Fci「eh罪tStats-esi由|DependentVariable:YMethod:LeastSquaresDate:06/05/12Time:13:04Sample:19902010Includedobservati-ons:21VariableCoefficientStd.Errort-Statisti-cProb.C3209711S31.833T-3.4444880.0031X60.0108870.00299836316500.0021X401204750.00608519.798020.0000X10.0198110.00631&31363620.0060R.-squared0.99B520Meandependentvar2559.848AdjustedR-squared0.998259S.D.depen(ientvar1476.614S.E.ofregression6161775Akaikeinfiocriterion11.24942Sumsquaredresiti64544.69Schwarzcriterion1144838Loglikeliho-o<j-114.1189F-statistic3822.854□urbin-Watsonstat2.089910Prab(F-statisti-c)0.000000Y=-3209・711+0・01089X6+0・1205X4+0・01981X1(■3.4449)(3.6317)(19.7980)(3.1363)RA2=0.9885RA2=0.9983F=3822.854D・W・=2・08991取=5%,由于n=21,k=4(包含常数项),查表得：dl=1.03,du=1.67由于dU<DW=2.08991<4-du，故：不存在序列相关性。拉格朗日检验View|PrtxzlDb]已ct|Print]NreE；|Fre;己ze|EMEFte]F5^cast:|Sfets]ResidsIBreusch-GodfreySerialCorrelationLNTest:F-statisticObs*R-squar&d0.11066&0144122Probability0.7438180.704217ProbabililyTestEquation:DependentVariable;RESIDMethod;LeastSquaresDate:06/09/12Time:13:20Presamplemissingvaluelaggedresidualssettozero.VariableCoefficientS