宏觀經(jīng)濟(jì)模型多種估計方法的EVIEWS實(shí)現(xiàn)

1、08統(tǒng)計學(xué)號：0807294吳揚(yáng)specification對話框輸入消費(fèi)方程， 在instrument list對話框輸入工具變量問題綜述建立中國宏觀經(jīng)濟(jì)模型。宏觀經(jīng)濟(jì)模型，是指以整個國民經(jīng)濟(jì)系統(tǒng)為研究對象，從總量水平和經(jīng)濟(jì)結(jié)構(gòu)方面來研究國民經(jīng)濟(jì)各變量之間的相互作用。它可用來評價宏觀經(jīng)濟(jì)政策、 分析宏觀經(jīng)濟(jì)結(jié)構(gòu)和國民經(jīng)濟(jì)的發(fā)展趨勢。宏觀經(jīng)濟(jì)模型的表達(dá)可以用單一方程進(jìn)行表達(dá)， 也可以用聯(lián)立方程組表達(dá)。本作業(yè)建立如下宏觀經(jīng)濟(jì)模型，完備的結(jié)構(gòu)式模型為Ct = o亠：:Yt亠：:2Ct1 hiI* |t = 00 + 0lYt + Pt2Yt =It+Ct+ Gt其中，包含3個內(nèi)生變量，即國內(nèi)生產(chǎn)總值

2、Y,居民消費(fèi)總額C和投資總額I;3個先決變量，即政府消費(fèi)G,前期居民消費(fèi)總額Ct-i和常數(shù)項(xiàng)?？梢耘袛?，消費(fèi)方程是恰好識別的方程，投資方程是過度識別的， 模型可以識別。數(shù)據(jù)來自題目提供。導(dǎo)入EVIEWS Group：UNTITLED Workfilet 6.4i:Untitled| 口；| 回訥已：Proc | Object PrintName Freeze Default Sort | Transpose Edit+/- |smpl+/obs-YIC01GobsYIC01jk197319783605.61377.91759.14686I197919794092.61478.92011.560

3、2.21198019&04592$1599.72331.2662.0119B119G15008.B163022627.9750.711982 19825590.01784.22902.9902.91198319636216.22039.03231,1946.H19B419847362.72515 1374201105.1198519859076.73457.54687.4931.811986196610506.53941.95302.11264J19B7198712277.4446205126.11S89；1983198815388.65700.2786B.11820J11989_19&917

4、311.36332.78312.62166.1199Q199019347.86747.09450.93149J11991199122577.47868.010730.63978.11992199227565.210086.313000.144781993199336938.115717.71641214808J1994199450217.420341.121844.28032.1QQAi rtn-Eana ndi vn i仃 QXC711QO|rpi卜各種方法的EVIEWS實(shí)現(xiàn)1.狹義的工具變量法估計消費(fèi)方程選取消費(fèi)方程中未包含的先決變量G作為內(nèi)生解釋變量Y的工具變量；在工作文件主窗口點(diǎn)擊qu

5、ick/estimate equation，選擇估計方法TSLS，在equationSpecifi cation OptionsEquation speci ficationSep end en t variable followed by list o regressors and FDL terms OR w. ftsplicitequation likecO l c y cOl (-1)ITLStrument list c g cOl c-l)include丄直芝岳電口rfigressors ior丄me ar equ&t 1 oms wi tM AKfflAEstimati on se

6、ttingsMethod TSLS - Twc-StageSquares (TSHLS and AENA)-1970 2009確走確走取消取消點(diǎn)擊確定，得到:Dependent Variable: C01Method: Two-Stage Least SquaresDate: 06/02/11 Time: 14:08Sample (adjusted): 1979 2009Included observations: 31 after adjustmentsInstrument list: C G C01(-1)VariableCoefficientStd. Errort-StatisticPr

7、ob.C1290.053402.73533.2032290.0034Y0.1071330.0231504.6277390.0001C01(-1)0.7857560.07185910.934710.0000R-squared0.998513Mean dependent var34025.26Adjusted R-squared0.998407S.D. dependent var34218.49S.E. of regression1365.679Sum squared resid52222209F-statistic9402.761Durbin-Watson stat0.743434Prob(F-

8、statistic)0.000000Second-Stage SSR53379247得到結(jié)構(gòu)參數(shù)的工具變量法估計量:?0=1290.053:?1=0.107133:?2二0.785756Equation EstimationR-squared0.998480 Mean dependent var34025.262.間接最小二乘法估計消費(fèi)方程 消費(fèi)方程中包含的內(nèi)生變量的簡化方程為Ct二二io+二iiCt-i+二12Gt+；tiYt二二20+二21Ct-1 +7. 22Gt+；t2參數(shù)關(guān)系體系為二11 - 1二21 -： 2 = 0二10 -： 0 _ ? 1二20 = 0二12 - 1二22 =

9、 0用普通最小二乘法估計第一個簡化式：C=二10+二nCt-1+二12G+；Dependent Variable: C01Method: Least SquaresDate: 06/02/11 Time: 14:46Sample (adjusted): 1979 2009Included observations: 31 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C1086.594386.55342.8109810.0089C01(-1)0.9545380.03625626.327720.0000G0.26558

10、10.0580214.5773100.0001Adjusted R-squared0.998372S.D. dependent var34218.49S.E. of regression1380.725Akaike info criterion17.39037Sum squared resid53379247Schwarz criterion17.52914Log likelihood-266.5507Hannan-Quinn criter.17.43561F-statistic9198.948Durbin-Watson stat0.743999Prob(F-statistic)0.00000

11、0用普通最小二乘法估計第二個簡化式：Y=二20+二2lCt-1+二22G+；t2Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 14:47Sample (adjusted): 1979 2009Included observations: 31 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C-1899.1342081.958-0.9121860.3695C01(-1)1.5754550.1952738.0679500.0000G2.478

12、9920.3124997.9327940.0000R-squared0.994318Mean dependent var84244.67Adjusted R-squared0.993912S.D. dependent var95306.59S.E. of regression7436.521Akaike info criterion20.75796Sum squared resid1.55E+09Schwarz criterion20.89673Prob(F-statistic)0.000000得到簡化式參數(shù)估計量為:?10 = 1086.594, 伽=0.954538, 7? = 0.265

13、581:?20=-1899.134, :?21=1.575455, :?22=2.478992由參數(shù)體系計算得到結(jié)構(gòu)參數(shù)間接最小二乘估計值為小J?12:?1=0.107132657J?22:?2= ?11-:?1?21=0.78575532:?0= ?10-:?1?20=1290.0532723.二階段最小二乘法點(diǎn)擊objects/new object，選擇systemLog likelihoodF-statistic-318.7484Hannan-Quinn criter.2449.755Durbin-Watson stat20.803200.686339Prob(F-statistic)0

14、.000000Estimation Method. OptionsSystem: UNTITLEDEstimation Method: Two-Stage Least SquaresDate: 06/02/11 Time: 15:09Sample: 1979 2009Included observations: 31Total system (balanced) observations 62CoefficientStd. Errort-StatisticProb.C(1)1290.053402.73533.2032290.0022C(2)0.1071330.0231504.6277390.0

15、000C(3)0.7857560.07185910.934710.0000C(4)-2538.266948.1448-2.6770870.0097C(5)0.4413900.00753458.585760.0000Determinant residual covariance1.63E+13Equation: C01=C(1)+C(2)*Y+C(3)*C01(-1)System EstimationTwoStage Least SquaresEstimation methodEstimation settingEIJ Add lg g電d regressors to instruments f

16、orlinear equatiOREI dent i ty wei ghting matrix inestimation QSLS coefs & OWNTime series HA specifi caticmL_ I Prewhi tening by VKR (1) Kernel BartlettQuadrati cBandwidth selectionNiimber or HWfor| Fixedn*AndrewsVar i abl e Sample1978 2009Mi誦Instruments: G C01(-1) CInstruments: G C01(-1) CObservatio

17、ns: 31R-squared0.998513Mean dependent var34025.26Adjusted R-squared0.998407S.D. dependent var34218.49S.E. of regression1365.679Sum squared resid52222209Durbin-Watson stat0.743434Equation: l=C(4)+C(5)*YObservations: 31R-squared0.991774Mean dependent var34646.51Adjusted R-squared0.991491S.D. dependent

18、 var42513.37S.E. of regression3921.722Sum squared resid4.46E+08Durbin-Watson stat0.538847消費(fèi)方程的參數(shù)估計量為:?0=1290.053:?1=0.107133:?2-0.785756投資方程的參數(shù)估計量為?0=-2538.266?1=0.4413904. 三階段最小二乘法System Estinnationon Method. OptionsEs.timeLtioik m電thod.Time series HAC specificationThreeStae Least Squares! Pr ewhi

19、tenin by VAR(1)Tf gfT| alJILCXsettings空Bartlett/ Add lagged rigressors toQudr ati cfor linearBwdviidthIdentity weightins matrix inFixed:nwNrnber or W forNewejWestestimation (25LS coef炭GMMAndrewsVariable - Hewtv-眈Sample1973 2009確定確定取取消消System: UNTITLEDInstruments: G C01(-1) CEstimation Method: Three-

20、Stage Least SquaresDate: 06/02/11 Time: 15:20Sample: 1979 2009Included observations: 31Total system (balanced) observations 62Linear estimation after one-step weighting matrixCoefficientStd. Errort-StatisticProb.C(1)1384.346361.67293.8276200.0003C(2)0.1165380.0181096.4351730.0000C(3)0.7563730.056038

21、13.497460.0000C(4)-2538.266917.0495-2.7678610.0076C(5)0.4413900.00728760.572280.0000Determinant residual covariance1.55E+13Equation: C01=C(1)+C(2)*Y+C(3)*C01(-1)Instruments: G C01(-1) CObservations: 31R-squared0.998459Mean dependent var34025.26Adjusted R-squared0.998349S.D. dependent var34218.49S.E.

22、 of regression1390.396Sum squared resid54129611Durbin-Watson stat0.672688Equation: I=C(4)+C(5)*YInstruments: G C01(-1) CObservations: 31R-squared0.991774Mean dependent var34646.51Adjusted R-squared0.991491S.D. dependent var42513.37S.E. of regression3921.722Sum squared resid4.46E+08Durbin-Watson stat

23、0.538847消費(fèi)方程的參數(shù)估計量為?0=1384.346?1=0.116538?2= 0.756373投資方程的參數(shù)估計量為?0=-2538.266?1=0.4413905. GMM （廣義矩估計）Adjusted R-squared0.998407 S.D. dependent var34218.49System EstimationKernelSample1978 20OT取消取消System: UNTITLEDEstimation Method: Generalized Method of MomentsDate: 06/02/11 Time: 15:27Sample: 1979 2

24、009Included observations: 31Total system (balanced) observations 62Identity matrix estimation weights - 2SLS coefs with GMM standard errorsKernel: Bartlett,Bandwidth: Fixed (3),No prewhiteningCoefficientStd. Errort-StatisticProb.C(1)1290.053616.41172.0928440.0408C(2)0.1071330.0277223.8645370.0003C(3

25、)0.7857560.0939578.3629010.0000C(4)-2538.2661067.430-2.3779230.0208C(5)0.4413900.01342532.878450.0000Determinant residual covariance1.63E+13J-statistic1.21E+13Equation: C01=C(1)+C(2)*Y+C(3)*C01(-1)Instruments: G C01(-1) CObservations: 31R-squared0.998513 Mean dependent var34025.26Estirriaticii MethodOptionsEstimation m啟thadGNM一Time ser i es (HJMJ)Prewhi tening by VAR (1)Estimation settingEAdd lagg電d regressors toULZ11-* tn t呂i jrLIR&i_cqiat: m二y Identi ty weighting matrix inestimation(2SLS