1、精選優(yōu)質(zhì)文檔-傾情為你奉上計量經(jīng)濟學實驗報告班級 金融 學號 1 姓名 一、 實驗名稱：城鄉(xiāng)居民消費、政府消費對經(jīng)濟增長的的影響的實證分析二、 實驗準備數(shù)據(jù)城鄉(xiāng)居民消費、政府消費對經(jīng)濟增長的影響的實證分析年份人均國內(nèi)生產(chǎn)總值城鎮(zhèn)居民消費支出總計農(nóng)村居民消費支出總計人均中央財政支出人均地方財政支出199223111671.7659.2199.89161219.48776199329982110.8769.65110.70648280.9926199440442851.31016.81146.38548336.937199550463537.571310.36164.74352398.636919

2、9658463919.51572.08175.77315472.77778199764204185.61617.15204.85173542.04294199867964331.61590.3250.52701614.98225199971594615.91577.4330.11066718.310462000785849981670.13435.51518817.92683200186225309.011741.1451.943551029.1365200293986029.921834.3527.173361189.6532003105426510.941943.3574.19115133

3、3.30112004123367182.12184.7607.292981584.20852005140537942.882555.4671.171491923.75952006161658696.552829.02760.102852315.08512007195249997.473223.85865.976432901.65620082269811242.93660.71007.08653693.6729三、 實驗步驟基本數(shù)據(jù)處理由于要對城鄉(xiāng)居民消費、政府消費對經(jīng)濟增長的的影響，所以對數(shù)據(jù)取對數(shù)設立回歸模型如下：LnYt =C+1Ln X1t +2Ln X2t +3Ln X3t +4Ln

4、X4tt+ Ut其中Y表示人均國內(nèi)生產(chǎn)總值；X1表示城鎮(zhèn)居民消費支出總計；X2 表示農(nóng)村居民消費支出總計；X3表示人均中央財政支出；X4表示人均地方財政支出；t=1992-2008。 從散點圖中可以看出lnx1、lnx2、lnx4與lny線性關系較明顯，lnx3與lny線性關系不明顯多重共線性檢驗運用OLS方法估計模型的參數(shù)，利用計量經(jīng)濟計算機軟件Eviews計算可得如下結果Dependent Variable: LNYMethod: Least SquaresDate: 12/14/10 Time: 22:38Sample: 1992 2008Included observations: 1

5、7VariableCoefficientStd. Errort-StatisticProb. C0.0.1.0.1863LNX10.0.4.0.0010LNX20.0.3.0.0059LNX3-0.0.-0.0.3650LNX40.0.7.0.0000R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criterion-5.Sum squared resid0. Schwarz criterion-4.Log likelihood49

6、.24635 F-statistic6288.743Durbin-Watson stat1. Prob(F-statistic)0.由于LnX3的參數(shù)估計未能通過t檢驗，而且符號的經(jīng)濟意義也不合理，故認為解釋變量間存在多重共線性在Eviews中計算解釋變量之間的簡單相關系數(shù)，得如下結果從表中可以發(fā)現(xiàn)LnX1與LnX2存在高度相關性。用Eviews找出最簡單的回歸形式Dependent Variable: LNYMethod: Least SquaresDate: 12/16/10 Time: 02:25Sample: 1992 2008Included observations: 17Vari

7、ableCoefficientStd. Errort-StatisticProb. LNX11.0.59.425630.0000C-1.0.-7.0.0000R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criterion-3.Sum squared resid0. Schwarz criterion-3.Log likelihood30.69305 F-statistic3531.405Durbin-Watson stat0.

8、Prob(F-statistic)0.Dependent Variable: LNYMethod: Least SquaresDate: 12/16/10 Time: 02:30Sample: 1992 2008Included observations: 17VariableCoefficientStd. Errort-StatisticProb. LNX21.0.28.232840.0000C-1.0.-3.0.0072R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regre

9、ssion0. Akaike info criterion-1.Sum squared resid0. Schwarz criterion-1.Log likelihood18.16341 F-statistic797.0931Durbin-Watson stat0. Prob(F-statistic)0. Dependent Variable: LNYMethod: Least SquaresDate: 12/16/10 Time: 02:31Sample: 1992 2008Included observations: 17VariableCoefficientStd. Errort-St

10、atisticProb. LNX30.0.15.792120.0000C4.0.13.681490.0000R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criterion-0.Sum squared resid0. Schwarz criterion-0.Log likelihood8. F-statistic249.3911Durbin-Watson stat0. Prob(F-statistic)0. Dependent V

11、ariable: LNYMethod: Least SquaresDate: 12/16/10 Time: 02:31Sample: 1992 2008Included observations: 17VariableCoefficientStd. Errort-StatisticProb. LNX40.0.25.673970.0000C3.0.19.988720.0000R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criter

12、ion-1.Sum squared resid0. Schwarz criterion-1.Log likelihood16.58107 F-statistic659.1528Durbin-Watson stat0. Prob(F-statistic)0.從以上表可以看出LnY受LnX1影響最大，其次LnX2，再次LnX3，受LnX4影響性對較小，因此選LnY=-1.+ 1.Ln X1為初始回歸模型進行逐步回歸。首先，在初始模型中引入LnX2得到如下表Dependent Variable: LNYMethod: Least SquaresDate: 12/16/10 Time: 02:40Sa

13、mple: 1992 2008Included observations: 17VariableCoefficientStd. Errort-StatisticProb. LNX11.0.7.0.0000LNX20.0.0.0.3437C-1.0.-7.0.0000R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criterion-3.Sum squared resid0. Schwarz criterion-3.Log likel

14、ihood31.25695 F-statistic1761.506Durbin-Watson stat0. Prob(F-statistic)0.盡管擬合優(yōu)度提高，但是LnX2的參數(shù)未能通過t檢驗，所以去掉LnX2.第二，將LnX4引入初始模型，可得到下表Dependent Variable: LNYMethod: Least SquaresDate: 12/16/10 Time: 13:10Sample: 1992 2008Included observations: 17VariableCoefficientStd. Errort-StatisticProb. LNX10.0.12.136

15、930.0000LNX40.0.4.0.0011C0.0.0.0.9571R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criterion-4.Sum squared resid0. Schwarz criterion-3.Log likelihood37.35711 F-statistic3617.809Durbin-Watson stat0. Prob(F-statistic)0. 可以看出引入LnX4,模型擬合優(yōu)度提高，且參

16、數(shù)符號合理，變量也通過t檢驗，所以LnX4保留。再引入LnX3可以得到下表VariableCoefficientStd. Errort-StatisticProb. LNX10.0.17.305760.0000LNX3-0.0.-3.0.0023LNX40.0.6.0.0000C-0.0.-0.0.6440R-squared0. Mean dependent var8.Adjusted R-squared0. S.D. dependent var0.S.E. of regression0. Akaike info criterion-4.Sum squared resid0. Schwarz

17、criterion-4.Log likelihood43.66927 F-statistic4711.074Durbin-Watson stat1. Prob(F-statistic)0.可以看出引入LnX3后擬合優(yōu)度再次提高且參數(shù)都通過t檢驗，所以LnX3保留。因此，最終的經(jīng)濟增長函數(shù)應以Y=f(Lnx1,LnX4,LnX3)為最優(yōu)，擬合結果如下：Lny= -0.+ 0.LnX1 -0.LnX3+ 0.LnX4 序列相關性檢驗 經(jīng)過調(diào)整后的模型變成：Lny= -0.+ 0.LNX1 -0.LNX3+ 0.LNX4 所以，只針對此模型進行序列相關性的分析。進行拉格朗日乘數(shù)檢驗,首先是二階的結果

18、如下:Breusch-Godfrey Serial Correlation LM Test:F-statistic1. Probability0.Obs*R-squared4. Probability0.其次進行三階的檢驗。其結果如下:Breusch-Godfrey Serial Correlation LM Test:F-statistic1. Probability0.Obs*R-squared4. Probability0.通過兩種檢驗的結果看，此模型不存在序列相關性問題，無需調(diào)整。異方差檢驗首先進行沒有交叉項的懷特檢驗，結果如下：White Heteroskedasticity Test:F-statistic1. Probability0.Obs*R-squared7. Probability0.再進行有交叉項的懷特檢驗，結果如下：White Heteroskedasticity Test:F-statistic0.y0.Obs*R-squared7.y0.通過檢驗。我們可以知道其Obs