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1、我國財政收入的多元線性回歸模型一、影響我國財政收入增長因素的實證分析研究財政收入的影響因素離不開一些基本的經(jīng)濟變量?;貧w變量的選擇是建立回歸模型的一個極為重要的問題。通過經(jīng)濟理論對財政收入的解釋以及對 實踐的觀察,對財政收入影響的因素主要有稅收、國內(nèi)生產(chǎn)總值和固定資產(chǎn)投資和社會消費品零售總額和社會總?cè)丝?,并且在總?cè)丝诶锩婵紤]了65歲以上的老年化人口數(shù)對稅收的負面影響。為了考察這一問題,從國家統(tǒng)計局的國家數(shù) 據(jù)里抽選出1995-2014年稅收、國內(nèi)生產(chǎn)總值、固定資產(chǎn)投資總額,社會消費品 零售額,社會總?cè)丝冢òɡ夏昊丝冢┑臄?shù)據(jù),利用 eviews7.2進行回歸分析,建立財政收入影響因素模型,分
2、析影響財政收入的主要因素及其影響程度。二、模型的設(shè)定1.將財政收入作為被解釋變量,用 丫表示。稅收,GDP固定資產(chǎn)投資總額、社會消費品零售額、社會總?cè)丝谧鳛榻忉屪兞?,分別用XI, X2, X3, X4, X5表示。2.數(shù)據(jù)性質(zhì)的選擇是:時間序列數(shù)據(jù)3.模型設(shè)定為: y cix-i 2x23X34x45X5 u三、數(shù)據(jù)收集如表財政收入(Y)(億 元)各種稅收(X1)(億元)國生產(chǎn)總 值GDP(X2)(億元)固定資產(chǎn) 投資總額(X3)(億元)社會消費 品零售總 額(X4)(億元)社會總 人口(X5) (萬人)65歲以上 的人口(萬人) (百分比)19956242.26038.0461129.820
3、019.3271896.11211217510(6.2%)19967407.996909.8271572.322913.5242842.81223897833(6.4%)19978651.148234.0479429.524941.12103071236268085(6.54%)19989875.959262.884883.728406.2183918.61247618359(6.7%)199911444.0810682.5890187.729854.7156998.41257868679(6.9%)200013395.2312581.5199776.332917.73132678.40126
4、7438821(7.0%)200116386.0415301.38110270.437213.49114830.101276279062(7.1%)200218903.6417636.4512100243499.9193571.601284539377(7.3%)200321715.2520017.31136564.655566.6179145.201292279692(7.5%)200426396.4724165.68160714.470477.468352.601299889857(7.6%)200531649.2928778.54185895.888773.6259501.0013075
5、610055(7.7%)200638760.234804.35217656.6109998.252516.3013144810419(8.0%)200751321.7845621.97268019.4137323.9448135.9013212910636(8.05%)200861330.3554223.79316751.7172828.443055.4013280210956(8.2%)200968518.359521.59345629.2224598.7739105.7013345011307(8.5%)201083101.5173210.79408903251683.7735647.90
68.9%)2011103874.4389738.39484123.5311485.1333378.1013473512288(9.1%)2012117253.52100614.28534123374694.7431252.9013540412714(9.4%)2013129209.64110530.7588018.8446294.0928360.2013607213161(9.7%)2014140349.74119158.05636138.7512760.723613.8013678213755(10%數(shù)據(jù)來源:國家統(tǒng)計局網(wǎng)四、參數(shù)估計:用eviews7.2做回歸分析。
7、假定模型中隨機項滿足基本假設(shè),可用OLS(最小二乘估計)法估計其參數(shù)。具體操作:(1) 打開 file-new-workfile,設(shè)置 start date 為 1995, end date 為2014,在命令框中輸入data y x1 x2 x3 x4 x5在命令框中輸入series x,x-i mean(x1) /stdev(x1)series x2x2 mean(x2)/stdevx2)series XsX3 mean(X3)/stdev(x3)series X4X4 mean(x4) /stdevx4)series x5X5 mean(X5)/stdev(X5)series yy me
8、an(y) /stdevy)將變量進行標(biāo)準化得bs5- 7- -8 i 2 3 4 5 ® 7 6 9 o 1 2 3 4 QJ- & 9 9&oooocioooool11T19 g 9 9 g o o o 町 5? s? o o o o o 11 1 113222322222-0,950705-0960293-1,004336-0.B445022.26M08-1.861462-0.924346-0945047-0 948819-0.8256711.S86435-1 saisfii-0.896236-909737-0 907046-0.8124781 464275-1
9、 32&475-0 668545-0 882305-0 878049-0.7&99331.121SaO-1 086973-oeasoae-0 844447-0 349850-0.7S050B0.77?5a4-0 868076,-0 786972-0 793813-0 79887376057&0.45702-0,665240-07211346-0 721208-0.74308107326280,225441-0.477153-0.664424-0 659024-0.686027-0.691725-0.050393-0.301399'-0.600352-0 5955
10、39-0603288-0.613213-0237579-0.136709*0,495000-0 484923-<1474096-0.5161964).3776150025214-0.376239-0 3619230.3410190.397151-0.4924670.1&8&27-0 215450-0 201246-O'172163-0.259053-0.5030950.335&6900 関 5650 0872040095590-GOBI 257-0.63993204&07700.2S4S63031656903546750.1497S3-0.7058
11、S20 6239690.4573860457834O'5082020.4865&a-07571010 7610490.7871190.3223530.3445960.66282 7-O.aOl9660.696239<1.2568031.2635581.2445051.0511925-03314101.0352681.5593121 5535611.5103271.463200-035S9921.1776161,8296461.81798017968631.929062-039652«1 3197512:0015282.0400262.0526922.36152&
12、amp;-0.9581121 470023¥XIX2X3X4X5在命令框中輸入Is y c x1 x2 x3 x4 x5即出現(xiàn)回歸結(jié)果Dependent Variable: Y Method: Least Squares Date: 11/13/15 Time; 21:31 Sample: 1995 2014Indudcd observation呂:20VanabieCoefficient Std. Error t-Statislic Prob.-2.15E-160.001442-1,49E-131.00001.0132710.1106859.1545470.0000-0.07640
13、80.152536-0.5009170.62420.090072D.0162065.5577fi90.0001-0.0255650.057889-0.4416190.6655-0.0503900.093742-0.5375390.5993R-squared0;999969Mean dependent var-3.88E-17Adjusted R-squared0.9999583.D. dep endent var1.000000SE of regression0.006443Aka ike info criterion-7X06801Sum squared resid6000582Schwar
14、z criterion-6708082Log likelihood76.06801Hanman-Quinn criler.-6,948468F-statistic91397.54Durbin-Watson stat2J13325P rob(F-stetistic)O.'OOOOOO根據(jù)表中的樣本數(shù)據(jù),模型估計結(jié)果為21.844306 1,013271x1 0.076408x2 0.090072x3 0.025565x4 0.050390花 uR20.999969 R20.999958 F=91397.54D.W=2.713325可以看出,可決系數(shù) R2 0.999969,修正的可決系數(shù)
15、 R2 0.999958,說明模型的擬合程度很好。但是0.05,x2、x4、x5系數(shù)均不能通過t檢驗,且均為負數(shù),與經(jīng)濟意義不符,表明模型很可能存在多重共線性。五、模型修正1.多重共線性的檢驗與修正(1)檢驗選中y,x1,x2,x3,x4,x5, 點擊右鍵,選擇“ open/as group ”,在出現(xiàn)的對話框里選擇“ View/CovarianeeAnalysis/correlation”,點擊 ok,得到相關(guān)系數(shù)矩陣”CorelationYXIX2X3X4X51,0000000'.&9S8620.9993 如0.993661-017323220.8937500.999362
16、1.0000000 9997030 991946-Q.7412470.8994080.9993900.9997031.0000000 991177-07465950.9047020.993661Ol9910460.9911771.000000-0.6650420.8593254).732022-0.7412474)748595-0.6650421 000000<0 953159018937500硼俶0.9047020359326-0,9&91691.000000由相關(guān)系數(shù)矩陣可以看出,y與x1、x2、x3的相關(guān)系數(shù)都在0.9以上,說明所選自變量是都與y高度相關(guān)的,用y與自變量做多
17、元線性回歸是合適的。 但解釋變量x1與x2、x3、x5之間存在較高的相關(guān)系數(shù),證明確實存在嚴重的 多重共線性。Y 對 XI、X2、(2) 多重共線性修正采用逐步回歸的辦法,檢驗和回歸多重共線性問題。分別作X3、 X4、 X5 的元回歸,在命令窗口分別輸入LS 丫 C X1,LS Y C X2,LS 丫 C X3, LS 丫 C X4, LS 丫 C X5 并保存,整理結(jié)果如 表所示變量X1X2X3X4X5參數(shù)估計值0.9998620.9993900.993661-0.7328220.893750T統(tǒng)計量254.9438121.420937.49995-4.5693838.453323R20.9
18、997230.9987810.9873610.5370280.798790R20.9997080.9987130.9866590.5113080.787611元回歸分析結(jié)果其中,為基礎(chǔ),順次加入其它變量逐X1的方程R2 =0.999708最大,以X1步回歸。 在命令窗口中依次入:LS Y C X1 X2,LS Y CX1 X3, LS Y C X1 X4,LSY C X1 X5并保存結(jié)果,整理結(jié)果如表所示。X1X2X3X4X5R2X1,X21.294516-0.2947420.999748(8.660291)(-1.971817)X1,X30.8853600.1154310.999929(58
19、.20682)(7.588867)X1,X41.0135540.0184720.999862(252.7904)(4.6070407)X1,X51.025910-0.0289610.999870(171.2237)(-4,833617)加入新變量的回歸結(jié)果(一)經(jīng)比較,新加入 X3的方程R2 =0.999929,改進最大,而且各個參數(shù)的t檢驗顯著,選擇保留X3。X2不能通過t檢驗,剔除X2。再加入其它新變量逐步回歸,在命令框中依次輸入:LS Y C X1 X3 X4,LS Y C X1 X3 X5保存結(jié)果,整理結(jié)果如表所示。加入新變量的回歸結(jié)果(二)X1X2X3X4X5R2X1,X3,X40.
20、9184710.0884830.0086060.99951(53.77340)(5.623360)(2.898046)X1,X3,0.9265900.086625-0.0140720.999961X5(52.00922)(5.688979)(-3.189324)當(dāng)加入X5時,R2有所增加,且t檢驗顯著,則選擇X5o再加入其他變量,在命令框中輸入LS 丫 C X1 X3 X5 X4加入新變量的回歸結(jié)果(三)X1X2X3X5X4R2X1,X3,X5,x40.9592090.088483-0.092190-0.0510120.999969(40.07464)(6.114166)(-2.214560)
21、(-1.885678)加入X4后,t值沒有顯著提高,反而有略微下降趨勢,R2也沒有顯著提高,剔除X4.Dependent Vanable: YMethod: Least SquaresDate: 11/15/15 Time: 17:05Sample: 1995 2014Included obscrvations; 20VariableCoefficientStd, Errort-StatisticProb,C-2.05E-160.001513J.35 曰131.D000X101265900,017S1652 00922OMOOX30;0366250.0152275.688979OjOOOOX5
22、-010140720.004412-3.189324Oj0057R-squared0.999961Mean dependent var-8.8BE-17Adjusted R-squared0.999954S.D. dependent var1.000000S-F. of regression036768Akike info criterion-6.976306Sum squared resid0M0733Schwarz criterion-6777160Log likelihood73.76306Hannan-Quinn enter.-6.937431F-statistic138251 2Du
23、rbin-Watson stat2.295833Prob(F-statistic)60000005X5,有回歸結(jié)果為所以修正多重線性影響后的模型為y c1X13X321.572480.926590X10.086625X3 0.014072X5T檢驗(52.00922)(5.688979)(-3.189324)2R =0.9999612R =0.999954F=138251.2 D.W=2.295833五、異方差檢驗在實際的經(jīng)濟問題中經(jīng)常會出現(xiàn)異方差這種現(xiàn)象,因此建立模型時,必須要注意異方差的檢驗,否則,在實際中會失去意義。由white檢驗得n R2 =18.62072,2分布表,得臨界值 差。
24、從上表可以看出,由 White檢驗可知,在=0.05下,查20.05 (9)=16.919,所以拒絕原假設(shè),表明模型存在異方Heteroskedasticity Test: WhiteF-statistic15.00040Prob. FQ,10)0 0001ObsR-squared18.62072prob. Chi-SquareO)0 0286Scaled explained SS34.650呂6Pfotx Chi-Square(9)O OOO1Test Equation:Dependent Variable: RESID*2 Methcxi: Le曰st SquaresDate; 11/1
25、7/1 5 Time; 20:57Sample: 1 995 2014 llIncJuded observations: 20VarifibleCoofficiQntStd, ErrorProb,C-0.0027320.000310-8.8008260 GOODXI-0.0027210.000546-4.9842540 0005XI 2-0.0041180.001133-3.6349210 0046X1'X30.0056320.0017973.1346250 0106X1*X50.0047560.0010134.6900350.0009X3-0.0033450.000517-6.466
26、9760 00O1X32-0.0032330.000793-4.0748440 0022X3"X50.0006360.0008560.7429290 4746X50.0054770.0006148.9253650 0000X52-0.0001467.43E-05-1.9658920,0777R-squaredCL931036Mean dependent var3.e6E-O5Adijiustdd R-squared0866969S D. depenctent var9 07E-O5S.E. of regression3,2eE - 05Akaike info criterion-17
27、.50418Sum squared resid1,0eE-08Schwarz criterion-17,00632Log liiikeliiilhooci165,0418Hannan-Quinn enter”-17,40699F-statistic15.00040Durbin*Watson stal2.711518P rob (F-s tatistic)0.000108(2)異方差的修正用 WLS估計:選擇權(quán)重 w=1/e1,其中e仁resid 。在命令窗口中輸入genr e1= resid,點回車鍵。在消除多重共線性后的回歸結(jié)果對話框中點擊Estimate/Op tio ns/Weithte
28、dLS/TSLS,并在Weight中輸入1/e1,點確定,得到如下回歸結(jié)果。Dependent Variable; YMethod; Least SquaresDate: 11/17/15 Time: 21:15Sample (adjusted): 1996 2012Included observations: 11 after adiustments Weighting series; 1ZE1Weight type; Varianee (average scaling)White heteroskedasticity-consistent standard errors & cov
29、ahancsVariableCoefficientStd. Errort-StatisticProb,C0.0047750.00046510.271300.0000X10.9423720.005478172*0315OOOODX30.0774650W508015.249080 0000X5-0.0195830.002614-7.4921850.0001WeightedStatisticsR-squared0.999999Mean dependent var0,052181Adjusted R-squsred0.999998S.D. dependenl vsr1.156455S-E- of re
30、gression0.001431Akaike info criterion-9.986177Sum squared resid1.43E-05Schwarz criterion-9.841488Log likelihood58,92398Hannan-Quinn crifter.-10.07738F-statistic1974410.Durbin-Watson stat1.539436Prob(F-statistic)O'.OOOOOOWeighted mean dep.0.341318Unweighted StatisticsR 十 squared0.999994Mean depen
31、dent var-0.187228Adiusted R-squared0.999991S.D. dependent var0.S99302S*E. of regression0.002672Sum squared resid5J00E-O5Durbin*Wat£on stat0.502795修正后的White檢驗HcfccTO-skmJasticiiY Tcbt; WhiteOba* R-SQuared Scokirl cKplaincd SSN ftWOZIW6 95&1OO1.&177S0PmhProtJ. cni-Square(4 Prob- CKi-SJ(J
32、1433O 1 302口33呂Test Equatjon.grxioni VArmhiA wtiT_HESio3 Lexist SquaresD#心 11/17/15 Time 225SrE削A: 1 夕ge 2012Inc;lulled otrservfitionfiz 1 1v«hebieCoefficl&nlStC# error1-St9bstcPeb-c WGT*2 X12*W<3T*22.O6E-OS -S.OQiE-Oe -3.02e 亠 OS 1 4E-O6 d.SeE-OG9.SaE-Cl72.225oao1 卻 EEQ 呂-1.373S3O2 QfiE
33、Y)密1.0513212 23EX)60.4&4 1641 .S&E-OG.2951 4o.oszz 02137 0 333母 o essm d.ossoR -SQuaredAdju石t辱d-squaredS.E. of regreaaion Sufn 呂qu旦r日a raskd Log llkjelinoodPrDb< 廠-SilM(i:(tic>O S32373O-3e7SeB1.1 IE-06 7.39e-l2 13日&4e鄉(xiāng) 2 SHD2-19O14C943Moan 寸口undent vi»r S.D <d&Mridnt va
34、r Ahaihft info crltenon Schwarz critaflon Hannan-Ouhnn enter. 口 rW rt I ftOrt SltH1 3OC-0Q1 qie_ae10076 -24.39563 315a972nR2 =6.9561<20.05=9.488,證明模型中異方差已經(jīng)被消除了。由white修正后的回歸結(jié)果Dependent Varkabte: Y Method Lst SquaresDitto 1 1/17/1S TlriTS S1;1iGShimplA facJ|Lj4tedl); 1996 2012Included observations&
35、#39; 11 after adjustnnents wwiRtiHrin $寸1 曰辱:1/F1Weight type: Variance (average Bcalinq)White hete no skod a si i city-wns »s1q nt smn 曲 E errors & cov4=*ri<*nce1VanableCoelticlentSto Errort-S(atl3tlcProb.C XI X3 X50.001 TV 50.9斗23720.077466 -0,0195830.000*16S10 271300.00 547S1 7Z.O31 50
36、.0050601 5 249Sa0 002614-7.43ie&0.0000 O OOOO 0.0000 0 0001Weighted StatisticsR-aquaredAdjiListed R-squared S.E_ of reqresion Sum wqum石d reE-id Lofl liKotihood F'StallstJcProtHF-sTati stk:>0.999990O.99S998 0.0014311 43Eq5 60 02300 1&?44FC>. 6000000Mean depandent vsr s.Dh dependent
37、war Akaiks mfo criterion Schwarz criterion Harmon-Quinn origr urun-Watson stat Wei口ht#iJ mean dep0.0521611 156455 -9.906177 -e.a4i4ea -IO1 S3S436O.34131SUrvwsightdStstivticBR-sguareeiAdlusttd f*l-a口U曰帕dS.E or reqressionDurhin-W&t&on stat0.5999940.0026720,502795Mea.n dapendOrkt var S.13. dm口日
38、n*d曰rd var Sum squared rasid-0.1 B7Z2aD.e3O25 OQE-O5異方差修正后的模型為0.004775 0.942372x10.077465x30.019583x5t 值 (10.27130)( 172.0315)( 15.24988)(-7.492185 )R2 = 0.9999992R2 =0.999998其中 xi = 1/e1* X1,X3 =1尼 1*X3,x5=1 尼1*X4 , e1=resid六、自相關(guān)檢驗(1)D.W檢驗在顯著水平0.05,查D.W表,當(dāng)n=20,k=3時,得上臨界值,* 1.68下臨界值dl 1.00,D.W.=1.53
39、9436.因為dl DW du,不能判斷誤差項存在自 相關(guān)。(2) LM檢驗按路徑“ View/Residual Tests/Serial Correlation LM Tests”,在出現(xiàn)的對話框中選擇Lags to include:1 ,點擊ok.得到LM檢驗結(jié)果如下。LM檢驗結(jié)果Breusch-Godfray Sarifil Correlation LM Test;F-statistic9.549596Prob. F(K6)0.0214Obs*R-sq uarad6.755517Prob. Ch»-Squar©1)0.0093Test Equatiori:Depend
40、eni Vanatie: RES id Method: Least SquaresDale: 11723/15 Time: Sample: 1996 201215:30Included observations*11Pre sample and interior missin 口 value laqqed residuals set to zero.Weight series: 1/E1Variabl'eCoefficientStd.匚rrort-SlatisticProb.C0.000256o,ooo3eeo.zoeoos0,5067XI-0.0003430,004/59-0.072
41、137O,944SX30.0005380.0039170.132610.0952X5-0.0002020,002:1-0.124597D.9049RESID 卜 1-0.414SQ70,393496-1.0541580.3324Weiqhted StatisticsR-squared0.614136dependent var-0.000235Adjusted R-squared0.356896S.D. dependent var0.001 159S.E. Of regression0.000960Akaike info criterion-10.75663Sum squared resid5+
42、53E-06Schwarz criterion-10.57577Loq likelliitiood64.16149Hannan-Quinn criter.-10.87064F-statistic2.337399Durbin-Watson stat1.1 I11 395P rob (F-stati Stic)0.163299Weighted mean dep.6 23E-17Unweiphted StatisticsR*squared-0.356465Mean dependent var-0.000834Adjusted R*squared-1.260775S.D. dependent var0
43、.002058S.E. of regression0.003094Sum squared resid5.74E-O5Durbin-Watson stat0.106365由于Prob(resid( 1) =0.3324>0.05 ,所以不存在自相關(guān)。由之前消除異方差,并檢驗出沒有自相關(guān)的回歸結(jié)果Dependent Variable: YMethod: Leat SquaresDate: 11/17/15 Time: 21:15Sample (adjusted): 1996 2012Included observations: 11 after adjustmentsWeiqhtinq se
44、ries: 1/E1WeighI type: Variance (average scaling)White heteroskedasticity-consislent standard errors & covarianceVariableCoefficientStd. Errort-StaftsticProb.C0.0047750.00046510.271300.0000X10.9423720-00547Q172X)3150.0000X30.0774650.00508015,249880.0000X5-0.0 II95830.002614-7.492165OuOOOlWeighte
45、d StatisticsR-squared0.999999Mean dpendnt var0.052181Adjusted R-squared039998S.D. dependent var1.156455S.E. of regression0.001431Akaik© info criterion-9.986177Sum squared resid1 43E-05Schwarz criterion-9 841488Log likelihood58.92390Hannan-Quirn enter.-10.07738F-statistic19744110.Durbin-Watson s
46、lat1.539436Prob(F-stalistic>0-000000Weighted mean dep.0.341318UnweightedStatisticsR-squared0.999994Mean dependent var-0.187228Adjusted R-squared0.999991S.D. dependent var0,899302SE. of regression0.002672Sumi squared rosid5.00E-05Durbin-Watson stat0.502795得出最終的回歸方程為0.004775 0.942372x1 0.077465x30.
47、019583疋t 值 (10.27130)( 172.0315)( 15.24988)( -7.492185)2R2 = 0.9999992R2 =0.999998其中 x1 = 1/e1* X1,x3 =1尼 1*X3,x5=1 尼1*X4 , e1=resid七、模型檢驗1、經(jīng)濟意義檢驗?zāi)P凸烙嫿Y(jié)果表明,在假定其他變量不變的情況下,當(dāng)稅收每增長 1 元時,財政收入增加 0.94237 元;在假定其他變量不變的情況下,當(dāng)固定資產(chǎn)總額每 增加 1 元時,財政收入增加 0.077465 元;在假定其他變量不變的情況下,當(dāng)社 會總?cè)丝诿吭黾?1 人時,財政收入減少 0.019583 元,這是基于前
48、面數(shù)據(jù)的老年 化人口數(shù)占總?cè)丝诘陌俜直炔粩嘣鲩L,隨著老年化人口的增多,給我國財政收入帶來了負效應(yīng),這與理論分析判斷相一致。2、統(tǒng)計檢驗(1)擬合優(yōu)度:由表中數(shù)據(jù)可得:R2=O.999999,修正的可決系數(shù)為R2=0.999998,這說明模型對樣本的擬合很好。(2) F檢驗:針對Ho : 135 0,給定顯著性水平0.05,在F分布表中查出自由度為 k=3 和 n-k-1=16 的臨界值 F (3 ,16)=8.69 。由表中得到F=1974410,由于F=1974410>F = ( 3 ,16)=8.69,應(yīng)拒絕原假設(shè),說明回歸 方程顯著,即“稅收”、“固定資產(chǎn)總額”、“社會總?cè)丝凇钡茸?/p>
49、量聯(lián)合起來 確實對“財政收入”有顯著影響。3、(3) t檢驗:分別對HO: j =0(j=1,2 ,3),給定顯著性水平a =0.05,查t分 布表得自由度為 n-k-1=16 臨界值 t /2 (n-k-1)=2.120 。由表中數(shù)據(jù)可得, )1 、 ) )5,對應(yīng)的 t 統(tǒng)計量分別為 172.0135、15.24988、-7.492185 ,其絕對值 均大于 t /2 (n-k-1)=2.120 ,這說明應(yīng)該分別拒絕 H0: j =0(j=1,2 , 3), 也就是說,當(dāng)在其他解釋變量不變的情況下,解釋變量“稅收”(X1) 、“固定資產(chǎn)總額”(X3)和“社會總?cè)丝凇狈謩e對被解釋變量“財政收
50、入”(Y)影響顯著。八、附表-S.aSE-171 OOOOOO-1 3S44ie-1 284343“ 3辜weO 050729表一:對X1的回歸結(jié)果表二:對X2的回歸結(jié)果Dependent Variable: V Method; L&sct Squares Dale; 11IM3/1S Time; 21 43Semple; 1995 201 Inoludecll observation: 20VanableCcAfficwniStd. ErrortlatlsucProb.C XI-2,22E-160 99062.003323-581E-MC OT3922254 94301 0000 0
51、 0000R-squared Adiusted R-squared 3.E. of r«gren£HM-i Sum aqijar&d re«ld Loq lik<iood F-atistic Protj(F-stalisliic)09997230.99970B 0170950 00526054 0540364996 3S . 000000Mean dependent var S,D, depernJerit war Aka ike info cnienon Schwarz crilerionHann*n Quinn crllw, Durtin-Wnfson stut-eR3E-1711 00000 -5 205408 5105834 5.1855700653879表三:對X3的回歸結(jié)果Dependent Variable Y Method: Least Squares Dale: 11 /13/15 Time: 21:44 Sample; 1995 2014Included observat
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