計(jì)量實(shí)習(xí)報(bào)告word版-資料下載頁(yè)
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【正文】 pendent varAdjusted Rsquared . dependent var. of regression Sum squared resid1643943.DurbinWatson stat三、全國(guó)各地區(qū)年人均通訊費(fèi)用支出與家庭可支配收入建立的線性回歸模型進(jìn)行異方差檢驗(yàn)及校正方法。GoldfeldQuandt檢驗(yàn)前10個(gè)數(shù)據(jù)的回歸Dependent Variable: YMethod: Least SquaresDate: 06/23/10 Time: 21:09Sample: 1 30Included observations: 30VariableCoefficientStd. ErrortStatisticProb. CXRsquared Mean dependent varAdjusted Rsquared . dependent var. of regression Akaike info criterionSum squared resid Schwarz criterionLog likelihood FstatisticDurbinWatson stat Prob(Fstatistic)GoldfeldQuandt檢驗(yàn)前10個(gè)數(shù)據(jù)的回歸Dependent Variable: YMethod: Least SquaresDate: 06/23/10 Time: 21:12Sample: 1 10Included observations: 10VariableCoefficientStd. ErrortStatisticProb. CXRsquared Mean dependent varAdjusted Rsquared . dependent var. of regression Akaike info criterionSum squared resid Schwarz criterionLog likelihood FstatisticDurbinWatson stat Prob(Fstatistic)RSS1=后10個(gè)數(shù)據(jù)的回歸Dependent Variable: YMethod: Least SquaresDate: 06/23/10 Time: 21:13Sample: 21 30Included observations: 10VariableCoefficientStd. ErrortStatisticProb. CXRsquared Mean dependent varAdjusted Rsquared . dependent var. of regression Akaike info criterionSum squared resid Schwarz criterionLog likelihood FstatisticDurbinWatson stat Prob(Fstatistic)Rss2=F=Rss2/Rss1=F(8,8)=利用WLS進(jìn)行消除(W=1/RESID)Dependent Variable: YMethod: Least SquaresDate: 06/23/10 Time: 21:16Sample: 1 30Included observations: 30Weighting series: 1/RESIDVariableCoefficientStd. ErrortStatisticProb. CXWeighted StatisticsRsquared Mean dependent varAdjusted Rsquared . dependent var. of regression Akaike info criterionSum squared resid Schwarz criterionLog likelihood FstatisticDurbinWatson stat Prob(Fstatistic)Unweighted StatisticsRsquared Mean dependent varAdjusted Rsquared . dependent var. of regression Sum squared residDurbinWatson stat實(shí)驗(yàn)五 序列自相關(guān)模擬實(shí)驗(yàn)一、實(shí)驗(yàn)?zāi)康模毫私庑蛄邢嚓P(guān)模型的檢驗(yàn)方法以及序列相關(guān)模型的處理方法。二、實(shí)驗(yàn)要求：（1）模擬線性回歸模型中隨機(jī)擾動(dòng)項(xiàng)為序列自相關(guān)的樣本數(shù)據(jù)，（2）進(jìn)行DW檢驗(yàn)；（3）利用Durbin兩步法進(jìn)行參數(shù)估計(jì)，建立模型三、實(shí)驗(yàn)結(jié)果報(bào)告：（圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫(xiě)報(bào)告）實(shí)驗(yàn)六 計(jì)量經(jīng)濟(jì)分析的創(chuàng)新性實(shí)驗(yàn)一、實(shí)驗(yàn)?zāi)康模禾岣哂?jì)量分析的創(chuàng)新能力。二、實(shí)驗(yàn)要求：（1）提出一個(gè)經(jīng)濟(jì)問(wèn)題；（2）提出經(jīng)濟(jì)模型；（3）收集相關(guān)數(shù)據(jù)并進(jìn)行檢驗(yàn)；（4）建立計(jì)量經(jīng)濟(jì)模型，并提出對(duì)策建議。三、實(shí)驗(yàn)結(jié)果報(bào)告：（圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫(xiě)報(bào)告）
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