計(jì)量經(jīng)濟(jì)學(xué)后習(xí)題Eviews實(shí)驗(yàn)報(bào)告

1、計(jì)量經(jīng)濟(jì)學(xué)實(shí)驗(yàn)報(bào)告實(shí)驗(yàn)一：EViews5.0軟件安裝及基本操作1.Eviews5.0的安裝過程解壓安裝包，雙擊“Setup.exe”，選擇安裝路徑進(jìn)行安裝；安裝完畢后，復(fù)制“eviews5.0破解文件夾”下的“eviews5.reg文件”和“eviews5.exe文件”到安裝目錄下；雙擊“Eviews5.reg”進(jìn)行注冊(cè)，安裝完畢。2.基本操作（數(shù)據(jù)來源于李子奈版課后習(xí)題P61.12）運(yùn)行Eviews,依次單擊filenewwork fileunstructedobservation 31。命令欄中輸入“data y gdp”，打開“y gdp”表，接下來將數(shù)據(jù)輸入其中。做出“y gdp”的散

2、點(diǎn)圖，依次單擊quickgraphscattergdp y。結(jié)果如下：開始進(jìn)行LS回歸：命令欄中輸入“l(fā)s y c gdp”回車，即得到回歸結(jié)果如下：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 09:38Sample: 1 31Included observations: 31VariableCoefficientStd. Errort-StatisticProb.  C-10.3934186.05105-0.1207820.9047GDP0.0710320.0074069.5912490.0

3、000R-squared0.760315    Mean dependent var621.0548Adjusted R-squared0.752050    S.D. dependent var619.5803S.E. of regression308.5175    Akaike info criterion14.36377Sum squared resid2760308.    Schwarz criterion14.45629L

4、og likelihood-220.6385    F-statistic91.99205Durbin-Watson stat1.570581    Prob(F-statistic)0.000000回歸方程為：Y = -10.39340931 + 0.07103165248*GDP 對(duì)回歸方程做檢驗(yàn)：斜率項(xiàng)t值9.59大于t在5%顯著水平下的檢驗(yàn)值2.045，拒絕零假設(shè)；截距項(xiàng)t值0.121小于2.045，接受零假設(shè)?？蓻Q系數(shù)0.76，擬合較好，方程F檢驗(yàn)值91.99通過F檢驗(yàn)。下面進(jìn)行預(yù)測：拓展工作空間：打開w

5、ork file窗口，單擊 ProcStructure，將End date的數(shù)據(jù)3132；確定預(yù)測值的起止日期：打開work file窗口，點(diǎn)擊QuickSample,填入“1 32”。打開GDP數(shù)據(jù)表，在GDP的最下方填，按回車鍵。在出現(xiàn)的Equation界面，點(diǎn)擊Forecast出現(xiàn)相應(yīng)界面如下：雙擊YF，得到y(tǒng)32=593.3756，預(yù)測完畢。實(shí)驗(yàn)二：回歸模型的建立與檢驗(yàn)（數(shù)據(jù)來源于李子奈版課后習(xí)題P105.11）運(yùn)行Eviews,依次單擊filenewwork fileunstructedobservation 10。命令欄中輸入“data y x1 x2”，打開“y x1 x2”表，

6、接下來將數(shù)據(jù)輸入其中。開始進(jìn)行LS回歸：命令欄中輸入“l(fā)s y c x1 x2”回車，即得到回歸結(jié)果：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 10:17Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb.  C626.509340.1301015.611950.0000X1-9.7905703.197843-3.0616170.0183X20.0286180.0058384.90

7、20300.0017R-squared0.902218    Mean dependent var670.3300Adjusted R-squared0.874281    S.D. dependent var49.04504S.E. of regression17.38985    Akaike info criterion8.792975Sum squared resid2116.847    Schwarz criterion8.

8、883751Log likelihood-40.96488    F-statistic32.29408Durbin-Watson stat1.650804    Prob(F-statistic)0.000292估計(jì)方程：依次單擊viewrepresentations，得到回歸方程為: Y = 626.5092847 - 9.790570097*X1 + 0.02861815879*X2，參數(shù)估計(jì)完畢。直接查看結(jié)果計(jì)算得到隨機(jī)干擾項(xiàng)的方差值為2116.847/（10-2-1）=309.55，可決系數(shù)為0.902，

9、修正后的可決系數(shù)為0.874。F=32.294>5%顯著水平下的F值4.74，即方程通過F檢驗(yàn)；兩個(gè)參數(shù)的t檢驗(yàn)值均通過了5%顯著水平下的t檢驗(yàn)值2.365。下面進(jìn)行預(yù)測：拓展工作空間：打開work file窗口，單擊 ProcStructure，將End date的數(shù)據(jù)1011；確定預(yù)測值的起止日期：打開work file窗口，點(diǎn)擊QuickSample,填入“1 11”。在x1的最下方填入35，在x2的最下方填入20000，按回車鍵。在出現(xiàn)的Equation界面，點(diǎn)擊Forecast出現(xiàn)相應(yīng)界面如下：雙擊YF,得到y(tǒng)11=856.2025，預(yù)測完畢。實(shí)驗(yàn)三：異方差、自相關(guān)、多重共線性

10、的檢驗(yàn)1.異方差檢驗(yàn)（數(shù)據(jù)來源于李子奈版課后習(xí)題P154.8）運(yùn)行Eviews,依次單擊filenewwork fileunstructedobservation 20。命令欄中輸入“data y x”，打開“y x”表，接下來將數(shù)據(jù)輸入其中。開始進(jìn)行LS回歸，命令欄中輸入“l(fā)s y c x”回車，即得到回歸結(jié)果如下：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 10:38Sample: 1 20Included observations: 20VariableCoefficientStd. Errort-Stat

11、isticProb.  C272.3635159.67731.7057130.1053X0.7551250.02331632.386900.0000R-squared0.983129    Mean dependent var5199.515Adjusted R-squared0.982192    S.D. dependent var1625.275S.E. of regression216.8900    Akaike info criterion13

12、.69130Sum squared resid846743.0    Schwarz criterion13.79087Log likelihood-134.9130    F-statistic1048.912Durbin-Watson stat2.087986    Prob(F-statistic)0.000000回歸方程為：Y = 272.3635389 + 0.7551249391*X開始檢驗(yàn)異方差圖示法：在工作文件窗口按Genr,在主窗口鍵入命令e2=resid2

13、，依次單擊QuickGraphScatter可得散點(diǎn)圖：顯然，散點(diǎn)不在一條水平直線上，即說明存在異方差性。White檢驗(yàn)法:依次單擊ViewResidual TestsWhite Heteroskedasticity因?yàn)楸绢}為一元函數(shù)，故無交叉乘積項(xiàng)，選no cross terms。經(jīng)估計(jì)出現(xiàn)white檢驗(yàn)結(jié)果，如下圖：White Heteroskedasticity Test:F-statistic14.63595    Probability0.000201Obs*R-squared12.65213    Pr

14、obability0.001789Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/11/11 Time: 11:16Sample: 1 20Included observations: 20VariableCoefficientStd. Errort-StatisticProb.  C-180998.9103318.2-1.7518580.0978X49.4284628.939291.7080060.1058X2-0.0021150.001847-1.1447420.2682R-sq

15、uared0.632606    Mean dependent var42337.15Adjusted R-squared0.589384    S.D. dependent var45279.67S.E. of regression29014.92    Akaike info criterion23.52649Sum squared resid1.43E+10    Schwarz criterion23.67585Log like

16、lihood-232.2649    F-statistic14.63595Durbin-Watson stat2.081758    Prob(F-statistic)0.000201所以拒絕原假設(shè)，表明模型存在異方差。 Goldfeld-Quanadt檢驗(yàn)法：在命令欄中直接輸入：sort x，得到按照升序排列的x。開始取樣本，依次單擊quicksample，填入“1 8”，回歸模型ls y c x；得到如下結(jié)果：Dependent Variable: YMethod: Least SquaresDate: 12

17、/11/11 Time: 11:26Sample: 1 8Included observations: 8VariableCoefficientStd. Errort-StatisticProb.  C1277.1611540.6040.8290000.4388X0.5541260.3114321.7792870.1255R-squared0.345397    Mean dependent var4016.814Adjusted R-squared0.236296    S.D. depend

18、ent var166.1712S.E. of regression145.2172    Akaike info criterion13.00666Sum squared resid126528.3    Schwarz criterion13.02652Log likelihood-50.02663    F-statistic3.165861Durbin-Watson stat3.004532    Prob(F-statistic

19、)0.125501繼續(xù)取樣本，依次單擊quicksample，填入“13 20”，回歸模型ls y c x；得到如下結(jié)果：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 11:28Sample: 13 20Included observations: 8VariableCoefficientStd. Errort-StatisticProb.  C212.2118530.88920.3997290.7032X0.7618930.06034812.625050.0000R-squared0.9637

20、23    Mean dependent var6760.477Adjusted R-squared0.957676    S.D. dependent var1556.814S.E. of regression320.2790    Akaike info criterion14.58858Sum squared resid615472.0    Schwarz criterion14.60844Log likelihood-56.3

21、5432    F-statistic159.3919Durbin-Watson stat1.722960    Prob(F-statistic)0.000015計(jì)算F統(tǒng)計(jì)量：F=RSS2/RSS1=615472.0/126528.3=4.864；F=4.864> F0.05（6,6）=4.28，拒絕原假設(shè)，表明模型確實(shí)存在異方差性。異方差的修正：在對(duì)原模型進(jìn)行OLS后，單擊QuickGenerate Series,在彈出的對(duì)話框內(nèi)輸w1=1/e,w2=1/e2。再選擇QuickEstimate Equat

22、ion,在彈出的對(duì)話框中選擇Options按鈕，在出現(xiàn)的畫面中，選中Weight Ls/TLS復(fù)選框，在 Weight內(nèi)分別輸入“w1”，“w2”，分別得下圖：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 11:33Sample: 1 20Included observations: 20Weighting series: W1VariableCoefficientStd. Errort-StatisticProb.  C415.6603116.97913.5532880.0023X0.7290

23、260.02242932.503490.0000Weighted StatisticsR-squared0.999895    Mean dependent var4471.606Adjusted R-squared0.999889    S.D. dependent var7313.160S.E. of regression77.04831    Akaike info criterion11.62138Sum squared resid106856.0 

24、0;  Schwarz criterion11.72096Log likelihood-114.2138    F-statistic1056.477Durbin-Watson stat2.367808    Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.981664    Mean dependent var5199.515Adjusted R-squared0.980645 

25、   S.D. dependent var1625.275S.E. of regression226.1101    Sum squared resid920263.9Durbin-Watson stat1.886959Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 11:34Sample: 1 20Included observations: 20Weighting series: W2VariableCoefficientStd. Errort

26、-StatisticProb.  C117.0597134.71860.8689200.3963X0.7869760.02605830.200730.0000Weighted StatisticsR-squared0.999999    Mean dependent var4207.516Adjusted R-squared0.999999    S.D. dependent var15774.21S.E. of regression15.35992    

27、;Akaike info criterion8.396039Sum squared resid4246.688    Schwarz criterion8.495613Log likelihood-81.96039    F-statistic912.0839Durbin-Watson stat2.113659    Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.980281   

28、; Mean dependent var5199.515Adjusted R-squared0.979185    S.D. dependent var1625.275S.E. of regression234.4844    Sum squared resid989692.9Durbin-Watson stat1.836717經(jīng)估計(jì)發(fā)現(xiàn)用w2=1/e2作為合適的權(quán)。再檢驗(yàn)：單擊QuickGenerate Series，分別輸入x1=x*w2,y1=y*w2,按住ctrl,依次點(diǎn)擊x1,y1，右鍵選擇Op

29、en as group，依次單擊QuickGraph可得下圖：由該圖可知，加權(quán)后X和Y的散點(diǎn)圖在同一直線上，所以是同方差性。2.自相關(guān)檢驗(yàn)（數(shù)據(jù)來源于李子奈版課后習(xí)題P155.9）運(yùn)行Eviews,依次單擊filenewwork fileAnnualstrat1980 end2007。命令欄中輸入“data y x”，打開“y x”表，接下來將數(shù)據(jù)輸入其中。開始進(jìn)行LS回歸，命令欄中輸入“l(fā)s log(y) c log(x)”回車，即得到回歸結(jié)果如下：Dependent Variable: LOG(Y)Method: Least SquaresDate: 12/11/11 Time: 11:4

30、9Sample: 1980 2007Included observations: 28VariableCoefficientStd. Errort-StatisticProb.  C1.5884780.13422011.834920.0000LOG(X)0.8544150.01421960.090580.0000R-squared0.992851    Mean dependent var9.552256Adjusted R-squared0.992576    S.D. dependent v

31、ar1.303948S.E. of regression0.112351    Akaike info criterion-1.465625Sum squared resid0.328192    Schwarz criterion-1.370468Log likelihood22.51875    F-statistic3610.878Durbin-Watson stat0.379323    Prob(F-statistic)0.0

32、00000杜賓瓦爾森檢驗(yàn)法：由結(jié)果得到，D.W值為0.379。查表得到dl=1.33,dw=1.48,D.W<dl,所以該模型存在序列相關(guān)性。圖形檢驗(yàn)法：單擊work file擊窗口Genr,分別輸入：e=resid e1=e(-1)；選中e、e1，右擊Open as Group,在Group窗口依次單擊Quick Graph Scatter，得到此圖：由上圖可知該模型存在序列相關(guān)性。3.多重共線性檢驗(yàn)（數(shù)據(jù)來源于李子奈版課后習(xí)題P155.10）運(yùn)行Eviews,依次單擊filenewwork fileunstructedobservation 10。命令欄中輸入“data y x1 x

33、2”，打開“y x1 x2”表，接下來將數(shù)據(jù)輸入其中。逐步回歸法：以Y為被解釋變量，逐個(gè)引入解釋變量。首先分別讓Y與x1與x2回歸：按照ls y c x1回歸：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 12:23Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb.  C244.545564.138173.8127910.0051X10.5090910.03574314.243170

34、.0000R-squared0.962062    Mean dependent var1110.000Adjusted R-squared0.957319    S.D. dependent var314.2893S.E. of regression64.93003    Akaike info criterion11.36135Sum squared resid33727.27    Schwarz criterion11.4218

35、7Log likelihood-54.80677    F-statistic202.8679Durbin-Watson stat2.680127    Prob(F-statistic)0.000001按照ls y c x2 回歸：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 12:26Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-Statist

36、icProb.  C238.994967.421463.5447900.0076X20.0498860.00366113.625160.0000R-squared0.958687    Mean dependent var1110.000Adjusted R-squared0.953523    S.D. dependent var314.2893S.E. of regression67.75602    Akaike info criterion11.4

37、4656Sum squared resid36727.03    Schwarz criterion11.50708Log likelihood-55.23280    F-statistic185.6448Durbin-Watson stat2.394519    Prob(F-statistic)0.000001對(duì)比可見x1的擬合結(jié)果較好，因此選用x1作為初始回歸模型加上x2再做回歸，按照ls y c x1 x2 回歸：Dependent Variable: YMetho

38、d: Least SquaresDate: 12/11/11 Time: 12:31Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb.  C245.515869.523483.5314080.0096X10.5684250.7160980.7937810.4534X2-0.0058330.070294-0.0829750.9362R-squared0.962099    Mean dependent var1110.000

39、Adjusted R-squared0.951270    S.D. dependent var314.2893S.E. of regression69.37901    Akaike info criterion11.56037Sum squared resid33694.13    Schwarz criterion11.65115Log likelihood-54.80185    F-statistic88.84545Durbi

40、n-Watson stat2.708154    Prob(F-statistic)0.000011很明顯，變量均未通過t檢驗(yàn)，因此，x1與x2之間存在共線性相關(guān)系數(shù)法：進(jìn)行LS回歸，命令欄中輸入“l(fā)s y c x1 x2”回車，即得到回歸結(jié)果：Dependent Variable: YMethod: Least SquaresDate: 12/11/11 Time: 12:34Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb. &

41、#160;C245.515869.523483.5314080.0096X10.5684250.7160980.7937810.4534X2-0.0058330.070294-0.0829750.9362R-squared0.962099    Mean dependent var1110.000Adjusted R-squared0.951270    S.D. dependent var314.2893S.E. of regression69.37901    Akaik

42、e info criterion11.56037Sum squared resid33694.13    Schwarz criterion11.65115Log likelihood-54.80185    F-statistic88.84545Durbin-Watson stat2.708154    Prob(F-statistic)0.000011在普通最小二乘法下，該模型的可決系數(shù)與F值較大，但是兩個(gè)參數(shù)估計(jì)值的t檢驗(yàn)值較小，說明各解釋變量對(duì)Y的聯(lián)合線性作用顯著，但

43、各解釋變量間存在共線性使得它們對(duì)Y的獨(dú)立作用不能分辨，故t檢驗(yàn)不顯著。實(shí)驗(yàn)四：聯(lián)立方程模型的估計(jì)與檢驗(yàn)（數(shù)據(jù)來源于李子奈版課后習(xí)題P228.8）運(yùn)行Eviews,依次單擊filenewwork fileunstructedobservation 18。命令欄中輸入“data m2 gdp p cons i”，打開“m2 gdp p cons i”表，接下來將數(shù)據(jù)輸入其中。建立統(tǒng)計(jì)模型：依次單擊objectnew objectsystem裝入模型：gdp=c(1)+c(2)*m2+c(3)*cons+c(4)*im2=c(5)+c(6)*gdp+c(7)*pinst cons i p估計(jì)模型：估計(jì)第一個(gè)方程：依次單擊quickestimate equationmethod勾選TSLS。輸入：gdp=c(1)+c(2)*m2+c(3)*cons+c(4)*icons i p得到下列結(jié)果：Dependent Variable: GDPMethod: Two-Stage Least SquaresDate: 12/11/1