在英留学时写的计量经济学论文(希望有帮助).docx

There are the main independent variables we research in this model. By testing this model, it can be concluded that which factors are significant for improving individual earnings, and know the relationships between them is positive correlation or negative correlation, or no linear correlation. Considering the validity of the model, the first step is to choose the more affect variables from the 22 variables. Referring to the research by others, the quality of male employee has far more influence on their income than other external factors from their wife or children ( ). So we select the following several variables: huseduc (years of schooling), husblck, husage, hushrs, hushisp, husexp, the independent variable is husearns.Some variables like the situation of economic development in 1991, external variables from wife and children, we are not able to quantify the size of influence, so we put them in the random fluctuations and fix them in heteroskedasticity test.Preliminary model: assumption: there is linear relationship between the dependent variable husearns and the independent variables huseduc, husage, hushrs, hushisp, husexp, husblck.Husearns=c+x1huseduc+x2husage+x3hushrs+x4hushisp+x5husexp+x6husblckUsing OLSDependent Variable: HUSEARNSMethod: Least SquaresDate: 01/01/11 Time: 16:01Sample: 1 5634Included observations: 5634VariableCoefficientStd. Errort-StatisticProb.C-464.433941.94402-11.072710.0000HUSAGE31.761252.16309114.683270.0000HUSEXP-31.509342.118723-14.871860.0000HUSBLCK-29.4199821.04346-1.3980580.1622HUSHISP23.9779420.873451.1487290.2507HUSHRS5.8247250.26662921.845790.0000HUSEDUC6.4272352.3895892.6896830.0072R-squared0.153373Mean dependent var453.5406Adjusted R-squared0.172492S.D. dependent var406.9878S.E. of regression370.2265Akaike info criterion14.66735Sum squared resid7.71E+08Schwarz criterion14.67559Log likelihood-41310.92F-statistic196.6973Durbin-Watson stat2.012364Prob(F-statistic)0.000000You can see from the table, R2=0.153373, which is not so significant. F-statistic=196.6973, which is so significant. When t0.05/2(n-k) =t0.025 (5634-6) =1.96, so the t-statistic of husblck and hushisp is not significant. And it is surprise that husexp have a negative correlation with the husearns. In addition, some variables have a close bond with each other (such as:husexp=huseduc-husage-6), so multicollinearity problem may exist.HUSAGEHUSBLCKHUSEARNSHUSEDUCHUSEXPHUSHISPHUSHRSHUSAGE1.000000-0.004729-0.079956-0.0506200.967514-0.058621-0.255914HUSBLCK-0.0047291.000000-0.056643-0.0843280.016832-0.048838-0.067556HUSEARNS-0.079956-0.0566431.0000000.310730-0.154993-0.0729460.333011HUSEDUC-0.050620-0.0843280.3107301.000000-0.301469-0.2563620.207321HUSEXP0.9675140.016832-0.154993-0.3014691.0000000.008931-0.296803HUSHISP-0.058621-0.048838-0.072946-0.2563620.0089311.000000-0.063873HUSHRS-0.255914-0.0675560.3330110.207321-0.296803-0.0638731.000000Table1Firstly, we can use the correlation matrix to see the coefficient. It can be seen from Table1, husexp and husage have a high coefficient. Now using stepwise regression method, to test and solve the problem of multiple linear. Analysis the independent variables and dependent variables respectively, you can get the table.1.huseduc2.husage 3.husexp4.hublck5.hushrs6.hushispcoefficient42.47738-2.8981-5.3635-97.473796.919034-119.3914t-statis24.53373-6.019722-11.77401-4.25771626.50414-5.488980R20.0965530.0063930.0.0240230.0032080.1108960.005321Adjust R20.0963930.0062170.0238500.0030310.1107380.005145R2 more to less: hushrs, huseduc, husexp, husage, hushisp, houseblckSo hushrs should be added in this model, then add other variables.variablest-statisticsR2hushrs huseduc22.64071 20.373180.171934hushrs husexp25.73064 -4.6889090.114354hushrs husage25.73064 0.445290.110926hushrs hushisp26.22403 -4.1271210.113578So huseduc should be added in this model, which improve the R2, t-value of this model.t-statisticsR2HushrsHuseducHusexp22.0127919.790160.3966970.171957HushrsHuseduchusage22.0127920.370570.3966970.171957HushrsHuseduchushisp22.6480319.937380.8295390.172035HushrsHuseduchusblck22.5427520.22391-1.3580460.172205From the above data it can be clearly seen, put any other variables, there is no help to improve their t- statistic of this model, so the model only retain two explanatory variables, huseduc and hushrs.The model heteroscedastic testFirstly, we can get the resid graphic. It can be seen from the graphic, the scatter appeared complex changes like deviation band, along with the increase of X. So the heteroscedastic may exist. However, in order to make sure whether it exist or not, we must do more test. Base on the big sample sizes, we can use white test to test hereroscedastic.White Heteroskedasticity Test:F-statistic193.7325Probability0.000000Obs*R-squared681.7618Probability0.000000Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 01/01/11 Time: 22:42Sample: 1 5634Included observations: 5634VariableCoefficientStd. Errort-StatisticProb.C123837.834104.023.6311800.0003HUSEDUC-30202.985428.793-5.5634800.0000HUSEDUC22114.943215.10419.8321830.0000HUSHRS-2237.899423.1717-5.2883940.0000HUSHRS260.942685.90861910.314200.0000R-squared0.121008Mean dependent var137135.8Adjusted R-squared0.120384S.D. dependent var262476.0S.E. of regression246170.6Akaike info criterion27.66632Sum squared resid3.41E+14Schwarz criterion27.67221Log likelihood-77931.04F-statistic193.7325Durbin-Watson stat2.045266Prob(F-statistic)0.000000From the table, nR2=681.7618, based on the assumption of white test, we can find the x2 in the distribution table, x2 0.