虛擬變量eviews實(shí)驗(yàn)報(bào)告

計(jì)量經(jīng)濟(jì)學(xué)——金融碩士實(shí)驗(yàn)報(bào)告專業(yè)班級(jí)學(xué)號(hào)姓名成績(jī)實(shí)驗(yàn)地點(diǎn)實(shí)驗(yàn)性質(zhì)：演示性驗(yàn)證性綜合性設(shè)計(jì)性實(shí)驗(yàn)項(xiàng)目名稱虛擬解釋變量回歸一、實(shí)驗(yàn)?zāi)康模赫莆仗摂M解釋變量回歸模型的估計(jì)與應(yīng)用，熟悉EViews的基本操作。二、實(shí)驗(yàn)內(nèi)容及要求：個(gè)人所得稅起征點(diǎn)調(diào)整對(duì)居民消費(fèi)支出會(huì)產(chǎn)生重要的影響。為研究個(gè)人所得稅起征點(diǎn)調(diào)整對(duì)城鎮(zhèn)居民個(gè)人消費(fèi)支出行為的效應(yīng)，收集相關(guān)的數(shù)據(jù)如表3.1和表3.2所示。表3.1個(gè)人所得稅起征點(diǎn)調(diào)整情況1987年1994年2006年2008年最低的起征點(diǎn)400元800元1600元2000元表3.2城鎮(zhèn)居民收入與消費(fèi)的有關(guān)數(shù)據(jù)城鎮(zhèn)家庭平均每人可支配收入（元）城鎮(zhèn)家庭平均每人全年消費(fèi)性支出（元）平均每戶城鎮(zhèn)家庭就業(yè)人口數(shù)（人）城鎮(zhèn)家庭平均每一就業(yè)者負(fù)擔(dān)人數(shù)（含本人）（人）1985739.1673.22.151.811986900.97992.121.819871002.1884.42.091.7919881180.211042.031.7919891373.9121121.7819901510.161278.891.981.7719911700.61453.81.961.7519922026.61671.71.951.7319932577.42110.81.921.7219943496.22851.31.881.7419954282.953537.571.871.7319964838.93919.51.861.7219975160.34185.61.831.7419985425.14331.61.81.75199958544615.91.771.7720006279.9849981.681.8620016859.65309.011.651.8820027702.86029.921.581.9220038472.26510.941.581.9120049421.67182.11.561.912005104937942.881.511.96200611759.458696.551.531.93200713785.819997.471.541.89200815780.811242.91.481.97若模型設(shè)定為：Consumet=Ct+α1Incomet+α2Consumet-1+α3Employmentt+α4Burdent+α5d1t+α6d2t+α7d3t+α8d4t+εt其中Consumet表示t期城鎮(zhèn)居民家庭人均消費(fèi)支出，Incomet表示t期城鎮(zhèn)居民家庭人均可支配收入，Employmentt表示t期城鎮(zhèn)居民家庭平均每戶就業(yè)人口，Burdent表示t期城鎮(zhèn)居民家庭平均每一就業(yè)者負(fù)擔(dān)人數(shù)，dit(i=1,2,3,4)相應(yīng)的虛擬變量。構(gòu)造用于描述個(gè)人所得稅調(diào)整的虛擬變量，并簡(jiǎn)要說(shuō)明其理由；考慮到個(gè)人所得稅起征點(diǎn)調(diào)整對(duì)居民消費(fèi)支出會(huì)產(chǎn)生重要的影響，而征稅有m=5個(gè)不同階段，故引入m-1=4個(gè)虛擬變量d1t，d2t，d3t，d4t。1985≤t<1987d1=0d2=0d3=0d4=01987≤t<1994d1=1d2=0d3=0d4=01994≤t<2006d1=1d2=1d3=0d4=02006≤t<2008d1=1d2=1d3=1d4=02008≤td1=1d2=1d3=1d4=1因?yàn)槭莻€(gè)人所得稅逐步增多的過(guò)程，所以在引入虛擬變量時(shí)，是使其在開(kāi)始實(shí)施后依然保留之前的稅收。用散點(diǎn)圖描述兩兩變量之間的關(guān)系，并給出你對(duì)模型設(shè)定的結(jié)論；=1\*GB3①導(dǎo)入數(shù)據(jù)打開(kāi)Eviews軟件，選擇“File”菜單中的“New--Workfile”選項(xiàng)（也可使用命令方式），建立工作文件后，錄入數(shù)據(jù)。命令格式：DATAincomeconsumemploymentburdend1d2d3d4=2\*GB3②觀測(cè)income、employment、burden同consum之間的關(guān)系，命令格式：SCATincomeconsumSCATemploymentconsumSCATburdenconsum根據(jù)相關(guān)關(guān)系圖，可以認(rèn)為，consum與income之間存在正相關(guān)的線性關(guān)系，consum與employment之間存在近似反比例的關(guān)系，consum與burden之間的關(guān)系圖比較混亂，認(rèn)為可能并不存在相關(guān)關(guān)系。依據(jù)測(cè)算，選擇你認(rèn)為更能描述客觀實(shí)際的模型，并簡(jiǎn)要說(shuō)明其理由；根據(jù)以上分析，可以得到如下模型：Consumet=Ct+α1Incomet+α2Consumet-1+α3log(Employmentt)+α5d1t+α6d2t+α7d3t+α8d4t+εt 在一定時(shí)期，技術(shù)固定，每戶就業(yè)人口越多，勞動(dòng)投入也就越多，相對(duì)的勞動(dòng)力工資就會(huì)越低，消費(fèi)也會(huì)降低，所以可以認(rèn)為每戶就業(yè)人口與消費(fèi)存在著反向關(guān)系。從散點(diǎn)圖也可以看出，兩者的關(guān)系有些類似于對(duì)數(shù)函數(shù)。而消費(fèi)與每一就業(yè)者負(fù)擔(dān)人數(shù)之間的關(guān)系圖比較混亂，認(rèn)為可能并不存在相關(guān)關(guān)系，剔除該變量。作如下回歸，命令格式：LSconsumCincomeconsum(-1)log(employment)d1d2d3d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:50Sample(adjusted):19862008Includedobservations:23afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C744.7966378.06621.9700170.0676INCOME0.6331180.03519817.987290.0000CONSUM(-1)0.0848730.0509071.6672210.1162LOG(EMPLOYMENT)-762.9720478.5280-1.5944140.1317D137.4346050.234450.7451980.4677D2221.076538.308405.7709660.0000D3-122.049373.81439-1.6534610.1190D4-178.868865.87071-2.7154520.0160R-squared0.999861Meandependentvar4428.906AdjustedR-squared0.999796S.D.dependentvar3060.917S.E.ofregression43.70477Akaikeinfocriterion10.66100Sumsquaredresid28651.61Schwarzcriterion11.05595Loglikelihood-114.6015F-statistic15413.79Durbin-Watsonstat2.977604Prob(F-statistic)0.000000由上圖可知，d1的伴隨概率為0.4677>0.05，不顯著，刪除變量d1，再作如下回歸，命令格式：LSconsumCincomeconsum(-1)log(employment)d2d3d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:53Sample(adjusted):19862008Includedobservations:23afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C871.9310332.66272.6210670.0185INCOME0.6299220.03444718.286760.0000CONSUM(-1)0.0835760.0501651.6660170.1152LOG(EMPLOYMENT)-889.4616441.1508-2.0162300.0609D2226.036137.197916.0765790.0000D3-110.888471.26752-1.5559460.1393D4-171.692464.25105-2.6722110.0167R-squared0.999856Meandependentvar4428.906AdjustedR-squared0.999802S.D.dependentvar3060.917S.E.ofregression43.09316Akaikeinfocriterion10.61040Sumsquaredresid29712.33Schwarzcriterion10.95598Loglikelihood-115.0196F-statistic18496.74Durbin-Watsonstat2.787479Prob(F-statistic)0.000000由上圖可知，d3的伴隨概率為0.1393>0.05，不顯著，刪除變量d3，再作如下回歸，命令格式：LSconsumCincomeconsum(-1)log(employment)d2d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:56Sample(adjusted):19862008Includedobservations:23afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C1204.936265.10544.5451220.0003INCOME0.5991650.02936620.403200.0000CONSUM(-1)0.0993140.0511471.9417090.0689LOG(EMPLOYMENT)-1325.942354.4143-3.7412220.0016D2251.367534.815737.2199400.0000D4-141.771063.81647-2.2215430.0402R-squared0.999834Meandependentvar4428.906AdjustedR-squared0.999785S.D.dependentvar3060.917S.E.ofregression44.85802Akaikeinfocriterion10.66434Sumsquaredresid34208.12Schwarzcriterion10.96056Loglikelihood-116.6399F-statistic20483.46Durbin-Watsonstat2.638666Prob(F-statistic)0.000000由上圖可知，consum(-1)的伴隨概率為0.0689>0.05，不顯著，刪除變量consum(-1)，再作如下回歸，命令格式：LSconsumCincomelog(employment)d2d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:59Sample:19852008Includedobservations:24VariableCoefficientStd.Errort-StatisticProb.C1460.937233.29226.2622630.0000INCOME0.6531010.00913271.515390.0000LOG(EMPLOYMENT)-1651.937314.1501-5.2584310.0000D2277.404833.627838.2492610.0000D4-154.274266.05969-2.3353770.0306R-squared0.999810Meandependentvar4272.418AdjustedR-squared0.999769S.D.dependentvar3090.239S.E.ofregression46.92598Akaikeinfocriterion10.71807Sumsquaredresid41838.91Schwarzcriterion10.96350Loglikelihood-123.6169F-statistic24