農(nóng)村居民家庭人均純收入影響因素分析報告

1、班級：商學院. :學號：指導教師：完成時間：年 月曰農(nóng)村居民家庭人均純收入影響因素分析摘要：隨著我國工業(yè)化與城市化建設的發(fā)展，農(nóng)村問題越來越凸顯， 留守問題、看病問題、養(yǎng)老問題等，農(nóng)民收入問題亦是國家各界人士 十分關注的問題。本文旨在用計量經(jīng)濟學方法簡單分析農(nóng)村居民家庭 人均純收入的影響因素。關鍵字：農(nóng)村居民家庭人均純收入 財政年度支農(nóng)支出 農(nóng)業(yè)機械 總動力 農(nóng)作物播種總面積 鄉(xiāng)村就業(yè)人數(shù) 鄉(xiāng)村人口數(shù)第一產(chǎn)業(yè)總產(chǎn)值正文：一、引言國家“十二五”規(guī)劃第六章拓寬農(nóng)民增收渠道中明確提出：加 大引導和扶持力度，提高農(nóng)民職業(yè)技能和創(chuàng)收能力，千方百計拓寬農(nóng) 民增收渠道，促進農(nóng)民收入持續(xù)較快增長。同時“十二五

2、”規(guī)劃中明 確提出以下幾點：1、穩(wěn)定糧食播種面積、優(yōu)化品種結構、提高單產(chǎn)和品質(zhì)。2、健全農(nóng)業(yè)補貼制度，堅持對種糧農(nóng)民實行直接補貼，繼續(xù)實行良 種補貼和農(nóng)機具購置補貼，完善農(nóng)資綜合補貼動態(tài)調(diào)整機制。3、推進農(nóng)業(yè)技術集成化、勞動過程機械化、生產(chǎn)經(jīng)營信息化。結合 這幾方面，本文從第一產(chǎn)業(yè)總產(chǎn)值、財政年度支農(nóng)支出、農(nóng)業(yè)機械總 動力等幾個方面分析其對農(nóng)村居民家庭人均純收入的影響。二、預設模型令農(nóng)村居民家庭人均純收入丫（元）為被解釋變量，農(nóng)作物播種 總面積X1（千公頃）、鄉(xiāng)村就業(yè)人數(shù)X2 （萬人）、鄉(xiāng)村人口數(shù)X3（萬 人）、第一產(chǎn)業(yè)總產(chǎn)值X4 （億元）、財政年度支農(nóng)支出X5 （億元） 農(nóng)業(yè)機械總動力X6（

3、萬千瓦）為解釋變量，據(jù)此建立回歸模型。三、數(shù)據(jù)搜集從中國統(tǒng)計年鑒得到如下數(shù)據(jù):年度農(nóng)村居農(nóng)作物鄉(xiāng)村就鄉(xiāng)村人第一產(chǎn)財政年農(nóng)業(yè)機民家庭播種總業(yè)人數(shù)口數(shù)X3業(yè)總產(chǎn)度支農(nóng)械總動人均純 收入Y （元）面積X1（千公 頃）X2（萬 人）（萬人）值 X4（億元）支出X5（億元）力X6（萬千瓦）1990686.3148362.347708841385062221.7628707.71991708.6149585.848026846205342.2243.5529388.61992784149007.148291849965866.6269.0430308.41993921.6147740.7485468534

4、46963.763323.4231816.619941221148240.648802856819572.695399.733802.519951577.7149879.3490258594712135.81430.2236118.0519961926.1152380.6490288508514015.39510.0738546.919972090.1153969.2490398417714441.89560.7742015.619982162155705.7490218315314817.63626.0245207.7119992210.3156372.8489828203814770.03

5、677.4648996.1220002253.4156299.8489348083714944.72766.8952573.6120012366.4155707.9486747956315781.27917.9655172.120022475.6154635.5481217824116537.021102.757929.8520032622.2152415475067685117381.71134.8660386.52420042936.4153552.469717570521412.71693.7964027.953120053254.9155487.4625874544224201792.

6、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、散點圖分析2、單因素或多變量間關系分析 Group: U樹TITLE D Workfi le: UIST

7、ITTLECXU ntitl ed| 口 I B 1 Z3ViewObjectj Rrir| Name F寸FFt Wmtu SpecIrCorrelation MatrixYX1X2X3X4X5X6Y1.QODDOOO.80D987-O.004B91-D.S6D23SD.59B239D.923B20D. 975356X109009371 000000-3.520B63-3.6823273.7830560.&35B661772623X2X).884B910 物 6631.0000000.918402-3.891367-1S60B63-1864062K3-0 S5D238刀 6823?70 91

8、34021 DDOOOO據(jù) M5376682731-3 961893X40 99B2390.7S3B560.891367-3.H53761 0000001&274SS0.972 翊M50 &23B200 695666-0 95DB63-D 88271D笠斑81 ooooooD B71277XS0 5793560.772623-3.964052-3.991D933.&T2&2416712771.000000L=Ulf由散點圖分析和變量間關系分析可以看出被解釋變量農(nóng)村居民家庭人均純收入丫（元）與解釋變量農(nóng)作物播種總面積X1、 鄉(xiāng) 村 就業(yè)人數(shù)X2、鄉(xiāng)村人口數(shù)X3、第一產(chǎn)業(yè)總產(chǎn)值X4、財政年度支農(nóng)支

9、 出X5、農(nóng)業(yè)機械總動力X6呈線性關系，因此該回歸模型設為：Y=B+B 1X1+B 2&+B 3X3+8 4X4+8 5X5+8 6X6+ 口3、模型預模擬用Eviews做OLS回歸分析得: Equation: UNTITLED Workfile: UNTITLEDUntitled| = | 回 | S3 |vie叫叩11亦時司月噲司 EsummtE | 5tats |Dependent Variable: YMethod: Least Squaresate: 06/03/12 Time: 21:58Sample: 1990 2010Included observations: 20Varia

10、bleCoefficientStd. Errort-StatisticProb.C-6067.3551991.962-3.0459340.0094X10.0202910.0114631.7701310 1001-0 0803230.066175-1.2213430.24360.071S620.0439411.630B670 12690 0936500.01S051S.2155320 00000.0029620.0430460.0638200.94620.0367970.0153282 4006210.0320R-squared0.998843Mean dependent var2626.790

11、Adjusted R-squared0.9963083.D. dependent var14B1.92OS.E. of regression60.94368Akaike info criteri&r11.32716Sumi squared rsid48291 64Schwarz criterion11 67566Log likelihood-106.2716F-statistic1869.907Durbin-Watson stat2.060182Prob(F-statisti：)C.000000Y=-6067.355+0.02029X1-0.08082X2+0.07165X3+0.09355X

12、4+0.002962X5+0.03680X6(-3.04593) (1.77013)(-1.2213 )(1.6307)(6.2155)(0.06882)(2.4006)R 2=0.99882=0.9983F=1869.907D.W.=2.0602五、模型檢驗1、計量經(jīng)濟學意義檢驗 （1）多重共線性檢驗與解決求相關矩陣得到: Group: UNTTLED Workfile: UMTTTUDXUrrthlad| 回S3 |Vi&xjFr匹 QbjectJ grhtn F=如 打ebI* S卜己匕| 5姑白| Sp丘匚 |coirralatlan MatrixYXIX2X3皿15X6Y1 0000

13、000.900937-3 664B91-3.&bD2393.9902393.&23D203.57935GX1O.80D9671.00DDOO-0.62BB63-0.682327D.783B560.6S5B66D.772B23X2-0 994B91-3 52BG631 0000003.910402-3.891367-3.&5DB63-3.064052X3-Q.J5D238-Q.6823270.91B4021.QODDOO-D.945376-D.S 32791-D.581B93X4O.990E3907830560.091367-3.94537S1.0000003.3274330.9 茂 9241X

14、50.923S200.695666-0L96DB63-D.882791D.9274S31.0QDDOOD.871277X60 打33580 宓 623-0 064052-3 931093。9蒞也I 871771 000000nrJ卜發(fā)現(xiàn)模型存在多重共線性。接下來運用逐步回歸法對模型進行修正:將各個解釋變量分別加入模型，進行一元回歸：作Y與X1的回歸，結果如下：Dependent Variable: Y Method: Least Squares ate: 06/03/12 Time: 22:16Sample: 1990 2010Included observations: 21Variable

15、CoefficientStJ. Error t-StatisticProb.C4S679.0G854&.810-5.6038320.0000X10.3334620.06S7175.904943o.oaooR-squared0.&S3408Mean dependent var2559.348Adjusted R.-squared0,635167S.D. dependent var147.614S.E. of re-gression891 89EDAkaike info criterion16一51497Sum squared resi-d15114052S-chwarz criterion1S.

16、61445Log likelihood-171 4072F-statist g35.81954urbin-Watso-n stat0.319004ProtjfF-statistic)0.000009作Y與X2的回歸，結果如下：rn Equation: EQ12 WarkFle: UWTITLED-Urititled = | 回 | 3 View I Proc I Object I PrintlName Freeze EEtimatE Forecast! Stats Resids IDependent Variable: Y Method: Least Squares ate: 06/03/12

17、 Time: 22:17 Sample: 1990 2010 Included observations: 21VariableCoeffi-cientStd. Error t-Statisti-cProb.C28798.133124.S7&.2157680.0000X2-0.55 S6520.056206-8.4056 6 80.0000R-squared0.788112Mean dependent var269.84SAdjusted R.-squared0.776961S.D. depen(F-statistic)o.oaoooo作Y與X3的回歸，結果如下:Q Equation; EQ1

18、3 WorkFile; UNTITLE&UntitledPrintNameFreeze IEstinna teForesca戒| StatsResids IDependent Variable: Y Method: Least Squares Date: OEJO3/12 Time: 22:17 S-ample: 1990 2010 Included obserYations: 20VariableCoefficientStd. Error t-Statisti-cProb.C20673.S61398 一 56614.782180.0000X3-0.22S9740.017613-12.9412

19、40.0000R.-squared0.902952Mean dependent var2526J90Adjusted R-squared0.897561S.D. depencient var1481.S20S.E. ofregressio-n474 3063Akaike infio criterion15.26 B22Sum squared res id4049379.Schwarz criterion15.35579Log likelihood-150 5622F-statistic167.4757Durbin-Wats-on statO.16460SProb(F-statisti-c)0.

20、000000作Y與X4的回歸，結果如下: Equation; EQ14 Wo-rkfile; UhlTFTLE&XUntitled = | 回 | 蹈 Vi巳w|Fggbj*t| Print Name Fr巳巳日吁| Ewtirmtel F口匚匚日耽 S1ats|N雨ds|Dependent Variable: Method: Least SquaresDate: OB/03/12 Time: 22:17Sample; 1990 2010Include-d observations; 21VariableCoefficientStd. Error t-Statisti-cProb.C-63.0

21、098842.22730-1.4921600.1521X40 1474330.0020810.838980.0000R-squared0.99622SMean dependent var25.59.848A-djusted R-squared0.996030S.D. dependent war1476.614S.E. of regression93.04413Akaike infio criterion11.99442Sum squared res id164487.0Schwarz criterion12.09390Log lilkelihood-123.9414F-statistic501

22、8.1B1Durbin-Wats-on stat1.241548Pro b(F-stati Stic0.000000作Y與X5的回歸，結果如下:View| Frcxz| Object PrintlNarne Freeze Estiirate Forecast Stats| Rjesidsepen-dent Variable: Y Method: Least Squares ate: 06/03/12 Time: 22:18Sample: 1990 2010Included observations: 21VariableCoefficientSt-d. Error t-StatisticPro

23、b.C1475.635163.15909.0441510.0000X50.621砌0 05906610.,523270.0000R-squarei0.853552Mean depenient 何2559.848Adjusted R-squared0 845844S.D. dependent varU76.&14S.E. of regression575 7576Akaike infio criterion15.65349Sum squared resid6386258.Schwarz criterion15.75297Lo-g likelihood-162.3&16F-statistic110

24、.7391urbin-Watson stat0.216775Pro b(F-stati Stic)0.000000E3作Y與X6的回歸，結果如下:Dependent Variable: YMethcxj： Least Squares ate: D&/03/12 Time: 22:18S-ample: 1990 2010Included observations: 21VariableCoefficientStd. Error t-StatiSticPro-b.C-1319 718191.8911-B.8774360.0000X60.07180S0.00333721.5155B0.0000R-s

25、quaredQ.9 網(wǎng) 574Mean dependent var2559.843Adjusted R-squared0.958459S.D. dependent 切1476.E14S.E. of regression300.8115Akaike info criterion14.34124Sum squared res id171K64Schwarz criterion14.44072Log likeliho-txi-148.5830F-statistic462.9200Durbin-Wats-or stat0.305528Prob(F-stati Stic)0.000000IO Eclja

26、Cir,; EQ16 WorkfiIe: UNTTLEDUntitled!=i而訕 Poc| dlbj&t| Prin t| Name I Freeze Estiniaite | F口resca st Stats | Reisjd s|依據(jù)可決系數(shù)最大的原則選取X6作為進入回歸模型的第一個解釋變量，再依次將其余變量分別代入回歸得:作Y與X6、X1的回歸，結果如下：底Proc| PbjEctl Print| NaEe|FrEEze| EstiEate|FerEca5iz|5tats|ResiddDependent Variable: Y Method: Least Squares Date: 0

27、6/09/12 Time: 12:50 Sample: 1990 2010 Include-d abseivati&ns: 21VariableCoefficientStd. Errort-StatisticProb.C7329.7664329.490-1.529860.1077X60 0660540 00526812.538360.0000X10.0412150 029&6J1.3894670.1E16R-squared0.964353Mean -dependent var2559.848Adjusted R-squared0-960437S.D. dependent var1476.E14

28、S.E. of regression29370*7Akaike info criterion14.33459Sum squared re aid1552724.Schwarz criterion14 43361Log likeihood-U7 5132F-statistic24JJ622Durbin-Watson stat0.298684.Pro b(F-stati Stic)0.000000作Y與X6、X2的回歸，結果如下: Equation; UNTITLED WorkFile;咪曝牝Untitled| 目 | S3 Vievvl Proc Object I Print Name Free

29、je| EstiE 曰足| Fcareca呂ResidslDependent Variable: Y Method: Least Squares ate: 06/09/12 Time: 12:51 Sample: 1990 2010 Included observations: 21VariableCoefficientStd. Erro-rt-StatisticProb.C3733.59528&5.54&1.3029270.2090 x&0.0620750.0063519.7734100.0000X2-0.0360370.054349-1.7B70410.0942R-squareJ0.966

30、402Mean dependent var2S59.B4SAdjusted R-squared0.962&6 9S.D. dependent var1476.614S.E &f regression285.2984Akaike info criterion14.27G51Sum squared resid146S113.Schwarz-criterion1442573Log likelihood-146.9034F-statistic258.8769Durbin-Watson sLat0.313811Pro bF-stati stic)0.000000作Y與X6、X3的回歸，結果如下:View

31、| Pec| 0切8國 Print可加忡氏花| EstimatEil Forecagt|stats|Rjesids|Dependent Variable: YMethod: Least Squares ate: D6J09/12 Time: 12:52S-ample: 1990 2010Included observations! 20VariableCoefficientSid. Errort-StatisticProb.C-8630.824567 土 9721.51 盟 190.1470X&0.0950590 0181965.2240610.0001X30.0764500.吒痢1 一283

32、 9450.2164R-squared0.962750Mean -dependent var2626790Adjusted R-squared0.958368S. D. dependent var1481.920S.E. of regressio-n302.3691Akaike info criterion14.39866Sum squared resid1554-260.Schwarz criterio-n14.54801Log likeliho-cxi-140.9866F-stati stic219.6908Durbin-Watson stat0.329402Pro b(F-stati S

33、tic)0.000000作Y與X6、X4的回歸，結果如下:O Equation: EQA4 WorkFile: 4Untitleda EMi已w| Proc| Dfaj已ct| Print| Name|Freeze EstiEate|Fermcawi:|tats|Rsjds|Dependent Variable: YMethcxi: Least SquaresDate: 06/0/12 Time: 12:S3Samiple: 1990 2010Include-d observations: 21VariableCoefficientStd. Errort-StatisticPro-b.C-29

34、3 889577.37295-3.7983500.0013X60.0120740 0036313.3253230.0038X40.1237420.00732116.903250.0000R-squared0.997663Mean dependent var2559.B48A-djusted R-squar&d0.997404S.D. dependent var1476.G14S.E. of regression7523755Akai Ice info criterion11.B1074Sum squared resid101892.4Schwarz criterion11.76996Log l

35、ikeliho-oi-118.9128F-stati stic3842 907Durbin-Watson stat1 252559Pro b(F-stati stic)0.000000作Y與X6、X5的回歸，結果如下:斷 Ew|Prx| Dhject| Print Name I Freese I Estinriate I Forecast! Stats I ResidslDependent Variable: Y Method: Least Squares Date: 06/09/12 Time: 12:53 Samiple: 1990 2010 Include-d observations:

36、 21VariableCoefficientS-td. Errort-StatisticProb.C-660.7700206j6408-3213Z250.0048X&0.0533260.00490210.877170.0000X50.194&620.0460194.3240260.0004R-squared0.980662Mean dependent var2559.848Adjusted R-squared0.978613S.D. dependent var1476.614S.E. of regressio-n216448SAkaike infio criterion13.72415Sum

37、squared res id843299.9Schwarz criterion13.87336Log likelih頑-141.1035F-statistic456.3973Durbin-Wat sen stat0.40E42Prob(F-statisti-c)o.oaoooo在滿足經(jīng)濟意義和可決系數(shù)的條件下選取X4作為進入模型的第二 個解釋變量，再次進行回歸則：作Y與X6、X4、X1的回歸，結果如下View | Pm c| 曰切己國 Print Vmrnei| Freeze | EstirnatEi| Forecast I Stats 職曲血|Dependent Variable: Y Me

38、thod: Least Squares Date: 0&/09/12 Time: 12:57 Sample: 1990 2010 Included observati-ons: 21VariableCoefficientSt-d. Errort-StatisticProb.C-3209.711931.83973+444880.0031x&0.0108870.0029983 6316500.0021X40.120+750.0060S519.798020.0000X10 0198110.0063163 1363620.0060R-squa.red0.998520Mean dependent var

39、2559 848A-djusted R-squared0.998259S.D. (dependent var1475.614S.E. of regression61.61775Akaike info criterion11.24942Sum squared res id64544 69Schwarz criterion11.44838Log ikelibDDd-114.1189F-statist ic3822 854Durbin-Watson stat2.089910Pro bF-stati stic)0.000000作Y與X6、X4、X2的回歸，結果如下 Equation: UNTFTLED

40、 Workfile:咪咪WJntitled 1=1 | 回 | S3 |ViEw Fhoc| Dbject| Print Name FEeze| EMEate| Forecast Ststs I Reside|epenient Variable: Y Method: Least Squares ate: 06/09/12 Time: 12:58 Sample: 1990 2010 Included observations: 21VariableCoefficientStd. Erro-rt-StatisticPro-b.C-981.7&68S20.997E-11958220 2482X60

41、0119720.003&633.2686370.0045X40.12S7980.00822515415230.0000X20.0135540.0161040.84166304117R-squareJ0.997767Mean dependent var2569.048Adjusted R-squareri0997361S.D. dependent var147G.614S.E. of regression76.85449Akaike info criterion11 515Sum squared resid97S1635Schwarz-criterion11.8G411L叫 likelihood

42、-110.4341F-statistic2520605Durbin-Watson stat1 396193Pro bF-statistic0.000000作Y與X6、X4、X3的回歸，結果如下切匠*| Pnx:|obj氐t| Print| P汽me Freere| Ewtirgte Foircastl Stats ResidMependent Variable: Y Method: Least Squares Date: 06/09/12 Time: 12:58Sample: 1990 2010Included observations: 50VariableCoefficientStri.

43、Errort-StatisticProb.C-2274.689141&.323-1.6050530.1278X50.0186360.006329Z.944-&580.0095X40.1226700.00734816.694560.0000X30.0208460.0146831.4196740.1749R-squared0.997978Mean dependent vsr2626.790Adjusted R-squared0.997538S.D. depen-dent war1481.920S.E. of regression72.62163Akaikc irfc criterion11.585

44、26Sum squared resid84382.41Schwarz criterion11.78441L&g likelihood-111.8626F-statistic2B31.907urbin-Watson stat1.561242P ro b(F-s tat i stic0.000000作Y與X6、X4、X5的回歸，結果如下O Equation: UNTITLED Workfile-: 4Untitledui 回Wew | Proc| Objgct| Print Name Freeze| EMemte | Fear的ca吐| tats ResidslDependent Variable

45、: Y Method: Least Squares ate: 06/0/12 Time: 12:59Sample: 1990 2010Included observations: 21VariableCoefficientStd. Errort-Stati sticProb.C-284-.8028T9.3ST43-3.5605960.0024X60.0130 D60 0039753.2720030.0046X401189970.01055811.270430.0000X50.0U3040.0225700.6337630.5347R-squared0.997717Mean -dependent

46、var2559 848AJj u sted R-s q u aredD 997315S.D. dependent var1476.614S.E. of regression76.62012Akaike info criterion11.&8263Sum squared res id99540.68Schwarz criterion11.8&158Log likelihood-118 一6676F-stati sti-c2476.846Durbin-Watson stat1170793Pro b(F-statistic)0.000000在滿足經(jīng)濟意義和可決系數(shù)的條件下選取X1作為進入模型的第三

47、個解釋變量，再次進行回歸則：作Y與X4、X6、X1、X2的回歸，結果如下Equation: UNTITLED Workf le-:映峰1=1 回吐| PrintPame|Fi匪回 Ewtigfe|F陡匚母|Stats|只曲血|Dependent Variable: YMethod: Least Squares ate: D&/09/12 Time: 13:01Sample: 1990 2010Included observations: 21VariableCoefficientStd. ErrorL-StatisticPro-b.C-3063.274946.4814-3.2399090.00

48、61XB0.0107400.00300B3.5724300.0026X40.1162170.00750715.480370.0000X10.0242150.0077843.110925O.OOE7X2-0.015&10.01&121-0.9714320.3458R.-squared0.998602Mean dependent var2669.848Adjusted R-squared0.998263S.D. dependent 盹r1476614S.E. of regressio-n61.72007Aka ike info criterion11.28735Sum squared res id

49、60949.87Schwarz criterion11.53E05Log likelih&od-113.5172F-statisti-c2867.E78Durbin-Wats-on stat2.132460Prob(F-stati Stic)0.000000T-作Y與X4、X6、X1、X3的回歸，結果如下CJ Equation: UNTITLED Worldlie-:咪咪dlJtitled1=1 回yiEwllVod 口切巳匚PrintName I Freeze I EEstimate I ForecastEtatsDependent Variable: Y4Method: Least Squ

50、aresDate: 06/09/12 Time: 13:02Sample: 1990 2010Included obseivati&ns: 20VariableCoefficientStd. Errort-Sta.ti sticPro-b.C-2973.97813Q8.2G3-2.2732270.0381X60 0099140 0069051.4357980.1716X40.1212040 005&2018.308850.0000X10 01953G0 0080162.2160140.0426X3-0.0019270.01G&990 11539B0.9097R-squared0.998476M

51、ean dependent var2626.790A-djusted R-squared0.998070S.D. dependent var1481.920S.E. of regression55.10023Akaike info criterion11.40205Sum squared res id63570.60Schwarz criterion11.B6098Log likelihtxxi-109.0205F-statistic2467.626Durbin-Watson stat2.213093Pro b(F-stati Stic)0.000000作Y與X4、X6、X1、X5的回歸，結果

52、如下Q Equation: UNTITLED Wc?kfiIm;咪咪4Utitledu 回4巨伸|尸口:| 口bj已匚PrintNaE|FrEEze| EM e ate | %旦由5土| Stats | Res-id |Dependent Variable: YMethod: Least SquaresDate: 06J09/12 Time: 13:03Sample: 19 9 0 2010Included obseivations: 21VariableCoefficientStd. Errort-Stati sticPro-b.C-332&.310926,4023-3.5905680.00

53、24X60.0122280.00317.33S536520.0014X40 1132340 00&58513.1898S0.0000X10.0206950.0052893.2905210.0046X50 0213890.0180941.1821050.2544R-squared0.998639Mean iependent var2559.B48A-djusted R-squared0.998298S.D. dependent var1476.E14S.E. of regression60.90998Akaike info criterion11.26093Sum squared resid59

54、360.41Schwarz criterion11.50962Log likelihood-113.2397F-stati stic2934.508Durbin-Watsen stat2.0E0097Pro b(F-stati Stic)0.000000可見加入其余任何一個變量都會導致系數(shù)符號與經(jīng)濟意義不符，故 最終修正后的回歸模型為：Y=-3209.71+0.01089X6+0.1205X4+0.01981X1(-3.4449)(3.6317)(19.7980)(3.1364)履2二0.99852=0.9983 F=3822.85 D.W.=2.0899異方差檢驗圖示法32與X6的散點圖如下

55、：說明e2與X6不存在異方差性。e2與X4的散點圖分析說明e2與X4不存在異方差性。廠2與X1的散點圖分析 Graph: UNITTLED Wcrkfile: IOUntitled| u | 回 | S3 |儺訕 Pec| Object| Printl Nmrnd AddTEKt| LinE/iShatJeTErnphtE| Z&orn|32000 -1280002400020000窟 1&000-120006000-。4000-oooQ _144-000152000160000 1l&400X1說明e2與X1不存在異方差性。G-Q檢驗對20組數(shù)據(jù)剔除中間五組剩下的進行分組后第一組(1990-

56、1997)數(shù)據(jù)的分析結果：O Equaticn: UNTITLED Workfile:咪;4UntitledView| Pmd 0切己國 Print Name | Freeze | EMrnate| F 口ecbs七 | Sts is | Ressids |Dependent Variable: Y Method: Least Squares ate: 0&/09/12 Time: S-ample: 1990 199-7 Included observations: E13:103VariableCoefficientStd. Error t-Stati sticProb.C4636.2671

57、143.983 40527400.0154X60.0274240.01125324369930.0714X40.09 配 90.0118250.2900000 0012X10.0270070.0035943.1065740.0360R.-squared0.998867Mean dependent var1239.425Adjusted R-squared0.990017S.D. dependent var561.3869S.E. of regression24.99686Akaike info criterion9.582230Sum squared res id2499 372Schwarz

58、 criterion9.621951Log likeliho-oKi-34.32892F-statistic1176.542Durbin-Wats-on stat2.286976Prob(F-stati sti-c)0.000002殘差平方和RSS1=2499.372第二組(2003-2010)數(shù)據(jù)的回歸結果:3 Equation: UNTITLED Workfile: SSSEUntitled=i 回Poi:|dEe吐| Print Name | Freeze | Etirnatg Fueh弱t| 5拍is 只應曲|Dependent Variable: YMethod: Least Squ

59、ares ate: 06/05/12 Time: 13:11Sample: 2003 2010Included observations: 3VariableCoefficientStd. Errort-Stati sticProb.C-3473.2863291.539-1.0552160.350SX60.0490560 0197752.4807480.0682X40.0715910.0300352 3835400.0757X10.0117010.0211900.5521810.6102R.-sq uared0.997113Mean dependent var4046.713Adjusted

60、R-squared0.994947S.D. dependent rar1155.640S.E. of regression82.14653Akaike info criterion11.96174Sum squared res id26992 25Schwarz criterion12.00146Log likelihio-oKi43.84696F-statistic460.4546 urbin-Wats-on stat1.770331ProbF-stati stic)0.000016殘差平方和RSS2=26992.25所以 F二RSS2/RSS1=26992.25/2499.372=10.7