1、精選優質文檔-傾情為你奉上計量經濟學實驗報告班級 金融 學號 1 姓名 一、 實驗名稱：城鄉居民消費、政府消費對經濟增長的的影響的實證分析二、 實驗準備數據城鄉居民消費、政府消費對經濟增長的影響的實證分析年份人均國內生產總值城鎮居民消費支出總計農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、 實驗步驟基本數據處理由于要對城鄉居民消費、政府消費對經濟增長的的影響，所以對數據取對數設立回歸模型如下：LnYt =C+1Ln X1t +2Ln X2t +3Ln X3t +4Ln

4、X4tt+ Ut其中Y表示人均國內生產總值；X1表示城鎮居民消費支出總計；X2 表示農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，利用計量經濟計算機軟件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的參數估計未能通過t檢驗，而且符號的經濟意義也不合理，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，得如下結果從表中可以發現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.盡管擬合優度提高，但是LnX2的參數未能通過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,模型擬合優度提高，且參

16、數符號合理，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后擬合優度再次提高且參數都通過t檢驗，所以LnX3保留。因此，最終的經濟增長函數應以Y=f(Lnx1,LnX4,LnX3)為最優，擬合結果如下：Lny= -0.+ 0.LnX1 -0.LnX3+ 0.LnX4 序列相關性檢驗 經過調整后的模型變成：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