計(jì)量經(jīng)濟(jì)學(xué)龐皓第三版課后答案解析-閱讀頁(yè)
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【正文】 RsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionAkaike info criterionSum squared residSchwarz criterionLog likelihoodHannanQuinn criter.FstatisticDurbinWatson statProb(Fstatistic)①得到模型的方程為：Y= X6+②評(píng)價(jià)：1） ，數(shù)據(jù)相當(dāng)大，可以認(rèn)為擬合程度很好。 X6 ，小于t（12）=，所以系數(shù)是不顯著的。但當(dāng)α=，t（24）=，LNCPI的系數(shù)不顯著，可能存在多重共線性。LNGDPLNGDP， LNCPI之間的相關(guān)系數(shù)很高，證實(shí)確實(shí)存在多重共線性。（4）建議：如果僅僅是作預(yù)測(cè)，可以不在意這種多重共線性，但如果是進(jìn)行結(jié)構(gòu)分析，還是應(yīng)該引起注意的。說(shuō)明在α=,水平下，回歸方程回歸方程整體上是顯著的。由此可得知，該方程可能存在多重共線性。（3）做輔助回歸被解釋變量可決系數(shù)方差擴(kuò)大因子CZZC353GDP90SSZE468方差擴(kuò)大因子均大于10，存在嚴(yán)重多重共線性。（4）解決方式：分別作出財(cái)政收入與財(cái)政支出、國(guó)內(nèi)生產(chǎn)總值、稅收總額之間的一元回歸。（2）存在異方差，估計(jì)參數(shù)的方法：①可以對(duì)模型進(jìn)行變換②使用加權(quán)最小二乘法進(jìn)行計(jì)算，得出模型方程，并對(duì)其進(jìn)行相關(guān)檢驗(yàn)③對(duì)模型進(jìn)行對(duì)數(shù)變換，進(jìn)行分析（3）評(píng)價(jià)：，隨機(jī)擾動(dòng)項(xiàng)之間不存在異方差。(1)由Eviews軟件分析得：Dependent Variable: YMethod: Least SquaresDate: 12/10/14 Time: 16:00Sample: 1 31Included observations: 31VariableCoefficientStd. ErrortStatisticProb.XCRsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionAkaike info criterionSum squared resid12220196Schwarz criterionLog likelihoodHannanQuinn criter.FstatisticDurbinWatson statProb(Fstatistic)由上表可知，2007年我國(guó)農(nóng)村居民家庭人均消費(fèi)支出（x）對(duì)人均純收入（y）的模型為：Y=+（2）①由圖形法檢驗(yàn)由上圖可知，模型可能存在異方差。（3）1）采用WLS法估計(jì)過程中，①用權(quán)數(shù)w1=1/X,建立回歸得：Dependent Variable: YMethod: Least SquaresDate: 12/09/14 Time: 11:13Sample: 1 31Included observations: 31Weighting series: W1VariableCoefficientStd. ErrortStatisticProb.XCWeighted StatisticsRsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionAkaike info criterionSum squared resid8352726.Schwarz criterionLog likelihoodHannanQuinn criter.FstatisticDurbinWatson statProb(Fstatistic)Unweighted StatisticsRsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionSum squared resid14484289DurbinWatson stat對(duì)此模型進(jìn)行White檢驗(yàn)得：Heteroskedasticity Test: WhiteFstatisticProb. F(2,28)Obs*RsquaredProb. ChiSquare(2)Scaled explained SSProb. ChiSquare(2)Test Equation:Dependent Variable: WGT_RESID^2Method: Least SquaresDate: 12/10/14 Time: 21:13Sample: 1 31Included observations: 31Collinear test regressors dropped from specificationVariableCoefficientStd. ErrortStatisticProb.C1045682.WGT^21173622.X*WGT^2RsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionAkaike info criterionSum squared resid+13Schwarz criterionLog likelihoodHannanQuinn criter.FstatisticDurbinWatson statProb(Fstatistic)從上可知，nR2=，比較計(jì)算的統(tǒng)計(jì)量的臨界值，因?yàn)閚R2=（2）=，所以接受原假設(shè)，該模型消除了異方差。估計(jì)結(jié)果為： Y= t=（）（）R2= F= DW=③用權(quán)數(shù)w3=1/sqr（x）,用回歸分析得：Dependent Variable: YMethod: Least SquaresDate: 12/09/14 Time: 21:35Sample: 1 31Included observations: 31Weighting series: W3VariableCoefficientStd. ErrortStatisticProb.XCWeighted StatisticsRsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionAkaike info criterionSum squared resid9990985.Schwarz criterionLog likelihoodHannanQuinn criter.FstatisticDurbinWatson statProb(Fstatistic)Unweighted StatisticsRsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionSum squared resid12717412DurbinWatson stat對(duì)此模型進(jìn)行White檢驗(yàn)得：Heteroskedasticity Test: WhiteFstatisticProb. F(2,28)Obs*RsquaredProb. ChiSquare(2)Scaled explained SSProb. ChiSquare(2)Test Equation:Dependent Variable: WGT_RESID^2Method: Least SquaresDate: 12/09/14 Time: 20:36Sample: 1 31Included observations: 31Collinear test regressors dropped from specificationVariableCoefficientStd. ErrortStatisticProb.C1212308.2141958.WGT^21301839.X^2*WGT^2RsquaredMean dependent varAdjusted Rsquared. dependent var. of regressionAkaike info criterionSum squared resid+13Schwarz criterionLog likelihoodHannanQuinn criter.FstatisticDurbinWatson statProb(Fstatistic)從上可知，nR2=，比較計(jì)算的統(tǒng)計(jì)量的臨界值，因?yàn)閚R2=（2）=，所以接受原假設(shè)，該模型消除了異方差。②White檢驗(yàn)用EViews軟件分析得：Heteroskedasticity Test: WhiteFstatisticProb. F(2,31)Obs*RsquaredProb. ChiSquare(2)Scaled explained SSProb. ChiSquare(2)Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 12/11/14 Time: 12:56Sample: 1 34Included observations: 34VariableCoefficientStd. ErrortStatisticProb.
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