農(nóng)村居民人均消費(fèi)性支出影響因素分析

農(nóng)村居民人均消費(fèi)性支出影響因素分析摘要:運(yùn)用Eviews軟件建立我國農(nóng)村居民全年人均消費(fèi)性支出的計(jì)量經(jīng)濟(jì)模型,對(duì)影響我國農(nóng)村居民全年人均消費(fèi)性支出的可能因素進(jìn)行分析,發(fā)現(xiàn)農(nóng)村居民全年人均純收入、農(nóng)村居民消費(fèi)價(jià)格指數(shù)、人均實(shí)際純收入、人均實(shí)際消費(fèi)性支出對(duì)我國農(nóng)村居民全年人均消費(fèi)性支出具有重要的影響關(guān)鍵字:農(nóng)村居民人均消費(fèi)性支出影響因素多元線性回歸一、問題的提出今年以來，全國上下認(rèn)真貫徹落實(shí)科學(xué)發(fā)展觀，以農(nóng)業(yè)增產(chǎn)、農(nóng)民增收為目的，加大各項(xiàng)惠農(nóng)政策措施落實(shí)力度，多措并舉做好農(nóng)村勞動(dòng)力轉(zhuǎn)移就業(yè)工作，克服金融危機(jī)和嚴(yán)重干旱等自然災(zāi)害帶來的不利影響，使全市農(nóng)村經(jīng)濟(jì)保持了穩(wěn)定發(fā)展的良好態(tài)勢，農(nóng)民現(xiàn)金收入持續(xù)增長，生活消費(fèi)水平繼續(xù)提高。我國是一個(gè)農(nóng)業(yè)大國，至今仍有9億農(nóng)村人口，占全國人口總數(shù)的70%，農(nóng)民是我國最大的群體，農(nóng)村消費(fèi)能力的提升直接關(guān)系到國民經(jīng)濟(jì)的全局。從農(nóng)村市場看，中國有近六成人口生活在農(nóng)村。農(nóng)村城鎮(zhèn)化的進(jìn)程對(duì)經(jīng)濟(jì)增長的帶動(dòng)作用是非常明顯的，世界上還沒有哪個(gè)國家有規(guī)模如此巨大的城鎮(zhèn)化。農(nóng)村居民的收入雖然低于城市居民，但是基數(shù)巨大，且農(nóng)村人口的收入也在穩(wěn)定增長。隨著經(jīng)濟(jì)的發(fā)展，我國農(nóng)民的收入水平和消費(fèi)水平的結(jié)構(gòu)也發(fā)生了很大變化，農(nóng)民生活水平的提高和消費(fèi)的增加對(duì)于實(shí)現(xiàn)國民經(jīng)濟(jì)又好又快發(fā)展、正確處理好內(nèi)需和外需的關(guān)系至關(guān)重要。但從總體來看，農(nóng)民消費(fèi)水平仍然較低，調(diào)查顯示有的地區(qū)都不及城市居民人均消費(fèi)支出的三分之一。而且消費(fèi)結(jié)構(gòu)不合理，局限于食品類等生存基本需求品，消費(fèi)在衣著裝飾等方面的極少。而影響農(nóng)民消費(fèi)水平的根本原因是農(nóng)民的收入。農(nóng)民生活消費(fèi)支出主要包括食品、衣著、醫(yī)療衛(wèi)生、教育文化、家庭設(shè)備、交通等方面，本文只挑選了五種典型的消費(fèi)支出作為代表來分析農(nóng)村居民的消費(fèi)結(jié)構(gòu)。二、理論依據(jù)（一）、1.三、模型設(shè)定（一）、影響因素的分析1、.農(nóng)民收入偏低，增收困難抑制消費(fèi)。消費(fèi)的多少在很大程度上決定于可支配收入，雖然農(nóng)村居民近幾年人均收入增長明顯，但相對(duì)仍處于較低水平。2、為下一代消費(fèi)支出過大影響其他消費(fèi)。一是教育費(fèi)用支出比重過大。調(diào)查顯示，高中、大學(xué)教育負(fù)擔(dān)沉重嚴(yán)重影響其他消費(fèi)支出。3、.醫(yī)療支出不確定性，使農(nóng)村居民不敢輕易消費(fèi)。農(nóng)村生活水平近幾年有大幅提升，在解決了基本溫飽問題之后，越來越多的農(nóng)村居民對(duì)醫(yī)療養(yǎng)老增加了關(guān)注度，對(duì)醫(yī)療保險(xiǎn)的投入度也較以往有所增加，特別是在新農(nóng)村合作醫(yī)療保險(xiǎn)開辦之后。4、主動(dòng)負(fù)債消費(fèi)理念還未普及，社會(huì)需求潛能被隱藏。改革開放后，我國居民長期保持著量人為出的消費(fèi)理念，特別是對(duì)于普通農(nóng)村居民長期以來形成了勤儉持家的習(xí)慣，消費(fèi)觀念較為保守，提倡“量人為出、知足常樂”的消費(fèi)觀念（二）、影響因素的選擇影響農(nóng)民人均生活消費(fèi)的因素有很多。經(jīng)分析有如下：變量選擇和說明：被解釋變量即自變量：農(nóng)民人均生活消費(fèi)支出F；解釋變量即因變量：農(nóng)民人均收入用，農(nóng)民人均食品消費(fèi)支出禺，衣著消費(fèi)支出用2,農(nóng)民人均交通和通訊消費(fèi)支出蠱3,農(nóng)民人均醫(yī)療保健消費(fèi)支出^4。（三）、模型形式的設(shè)計(jì)為此設(shè)定了如下對(duì)數(shù)形式的計(jì)量經(jīng)濟(jì)模型：=pi+p2Xt+p3X2t+p4X3t+p5X4t+|it其中農(nóng)民人均生活消費(fèi)支出農(nóng)民人均收入農(nóng)民人均食品消費(fèi)支出X2衣著消費(fèi)支出X3農(nóng)民人均交通和通訊消費(fèi)支出X4農(nóng)民人均醫(yī)療保健消費(fèi)支出咸----隨機(jī)干擾四、數(shù)據(jù)的收集一）、全國各地區(qū)農(nóng)村基本情況—人均消費(fèi)情況（2011）單位：元指標(biāo)農(nóng)村居民家庭人均收入X農(nóng)村居民家庭平均每人生活消費(fèi)支出F食品衣著交通和通訊醫(yī)療保健全國合計(jì)6977.35221.12107.3341.3547436.8

北京市14735.711077.73593.5862.61228.21035.2天津市12321.26725.42376611.7781.6571.7河北省7119.74711.21579.7334.1520.2434.7山西省5601.445871729.9401.9458.8349.3內(nèi)蒙古自治區(qū)6641.65507.72067395.2728.9534.2遼寧省8296.55406.42116.3446.1577.7482.9吉林省7510.05305.81872.1397.5564.3673.6黑龍江省7590.75333.62072.4473.8576.3573.6上海市16053.811049.34517.2644.51308.9908.6江蘇省10805.08094.62839.9554.8923.9645.6浙江省13070.79965.13714.8717.51380.6921.3安徽省6232.24957.32055.2297475.2440.5福建省8778.66540.93032.2395.4728.5321.2江西省6891.64659.92106.3233.6393.3346.7山東省8342.15900.62107.1399.8753.1508.4河南省6604.043201559.7362.8427.9399.7湖北省6897.95010.71954.6272.1414.4438.2湖南省6567.15179.42343.1260.4421.7396.5廣東省9371.76725.63301.1277.3682.5398.5廣西壯族自治區(qū)5231.34210.91844.9123.9384.8301.3海南省6446.04166.12137.9139.8370.3290.1重慶帀6480.44502.12108.6309401.7375.3四川省6128.64675.52161.7281.9431.1413.1貴州省4145.43455.81646.5186.2304.5246.3云南省4722.03999.91884209.1393309.3西藏自治區(qū)4904.32741.61384.7331.2348.965.8陜西省5027.94491.71345285.4406.7533.4甘肅省3909.43664.91548.2246.7366.6339.3青海省4608.54536.81716.4347.5450.9308.1寧夏回族自治區(qū)5410.04726.61762.5380483.4444.7新疆維吾爾自治區(qū)5442.24397.81589.5372.1530.6376.9二）、農(nóng)村居民家庭基本情況（1990-2011）單位：元指標(biāo)19901995200020102011調(diào)查戶數(shù)（戶）66960.0067340.0068116.0068190.0073630.00調(diào)查戶人口（人）平均每戶常住人口4.804.484.203.953.90平均每戶整半勞動(dòng)力2.922.882.762.852.78平均每個(gè)勞動(dòng)力負(fù)擔(dān)人口（含本人）1.641.561.521.391.40平均每人年收入（元）總收入990.382337.873146.218119.519833.14工資性收入138.80353.70702.302431.052963.43家庭經(jīng)營收入815.791877.422251.284937.485939.79財(cái)產(chǎn)性收入35.7940.9845.04202.25228.57轉(zhuǎn)移性收入65.77147.59548.74701.35現(xiàn)金收入676.671595.562381.607088.768638.51工資性收入136.43352.88700.412427.892959.74家庭經(jīng)營收入481.191116.731498.813955.364810.37財(cái)產(chǎn)性收入59.0538.1938.89168.33185.76轉(zhuǎn)移性收入87.76143.49537.18682.64純收入686.311577.742253.425919.016977.29工資性收入138.80353.70702.302431.052963.43家庭經(jīng)營純收入518.551125.791427.272832.803221.98財(cái)產(chǎn)性收入}40.9845.04202.25228.57J28.96轉(zhuǎn)移性收入57.2778.81452.92563.32平均每人年支出（元）總支出903.472138.332652.426991.798641.63家庭經(jīng)營費(fèi)用支出241.09621.71654.271915.622431.05購置生產(chǎn)性固定資產(chǎn)20.2962.3363.90193.26265.75稅費(fèi)支出38.6688.6595.528.5711.67消費(fèi)支出584.631310.361670.134381.825221.13財(cái)產(chǎn)性支出18.8055.2819.7449.2512.27轉(zhuǎn)移性支出148.86443.27699.76現(xiàn)金支出639.061545.812140.376307.437984.94家庭經(jīng)營費(fèi)用支出162.90454.74544.491757.582269.19購買生產(chǎn)性固定資產(chǎn)20.4662.3263.91193.26265.75稅費(fèi)支出33.3776.9689.818.5611.65消費(fèi)支出374.74859.431284.743859.334733.35財(cái)產(chǎn)性支出47.5992.359.8249.2512.27轉(zhuǎn)移性支出147.60439.45692.73五、模型的估計(jì)與調(diào)整（一）、模型估計(jì)1、農(nóng)村家庭總收入單線圖，農(nóng)村家庭總收入逐年增加。（X-農(nóng)村家庭總收入Y-年份）2、利用Eviews軟件，輸入Y、X、X2、X3、X4、X5等數(shù)據(jù)，采用這些數(shù)據(jù)對(duì)模型進(jìn)行OLS回歸，結(jié)果如表：DependentVariable:YMethod:LeastSquaresDate:06/14/14Time:22:01Sample(adjusted):132

Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-136.9270154.0293-0.8889670.3822X0.0245480.0418350.5867710.5624X11.0650020.1451907.3352420.0000X21.7912960.6041662.9649080.0064X31.5035120.1796868.3674600.0000X41.5841930.4843303.2709000.0030R-squared0.992351Meandependentvar5495.281AdjustedR-squared0.990880S.D.dependentvar1995.508S.E.ofregression190.5708Akaikeinfocriterion13.50529Sumsquaredresid944248.1Schwarzcriterion13.78011Loglikelihood-210.0846Hannan-Quinncriter.13.59638F-statistic674.6076Durbin-Watsonstat1.933440Prob(F-statistic)0.000000圖1由此可見，該模型R2=0.9924,F=674.608則，我國農(nóng)村居民全年人均消費(fèi)性支出模型的估計(jì)式為：Y=-136.927+0.02455Xt+1.065Xlt+1.7913X2t+1.5035X3t+1.5842X4t+“t（二）、模型檢驗(yàn)1、經(jīng)濟(jì)意義檢驗(yàn)。模型估計(jì)結(jié)果說明:農(nóng)村居民全年人均純收入、農(nóng)村居民消費(fèi)價(jià)格指數(shù)、人均實(shí)際消費(fèi)性支出的增加都將帶來我國農(nóng)村居民全年人均消費(fèi)性支出的增加，與理論分析和經(jīng)驗(yàn)判斷一致。該模型通過了經(jīng)濟(jì)意義上的檢驗(yàn)，系數(shù)符號(hào)均符合經(jīng)濟(jì)意義。2、統(tǒng)計(jì)意義檢驗(yàn)。R2=0.9924說明模型的擬合優(yōu)度較好，F(xiàn)=674.608符合F檢驗(yàn)，因而農(nóng)民人均收入、農(nóng)民人均食品消費(fèi)支出、衣著消費(fèi)支出、農(nóng)民人均交通和通訊消費(fèi)支出、農(nóng)民人均醫(yī)療保健消費(fèi)支出五個(gè)解釋變量對(duì)農(nóng)村居民全年人均消費(fèi)性支出的99.2%的離差做出解釋，且解釋變量聯(lián)合起來對(duì)被解釋變量有顯著影響。3、多重共線性的檢驗(yàn)X、X1、X2、X3、X4的相關(guān)系數(shù)如表：XX1X2X3X4X10.79440.74500.74590.7258X10.794410.65360.74500.7705X20.74500.653610.78580.7830X30.74590.74500.785810.7500X40.72580.77050.78300.85001表2通過簡單相關(guān)系數(shù)檢驗(yàn)法：由表2可知任意兩個(gè)解釋變量之間的零階相關(guān)系數(shù)V0.8。由此知該模型不存在多重共線性用Y分別對(duì)X、X2、X3、X4、X5作一元線性回歸，結(jié)果如圖：圖2-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:20Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C735.9259290.90292.5297990.0169X0.6375970.03625817.584760.0000R-squared0.911563Meandependentvar5495.281AdjustedR-squared0.908615S.D.dependentvar1995.508S.E.ofregression603.2415Akaikeinfocriterion15.70297Sumsquaredresid10917008Schwarzcriterion15.79458Loglikelihood-249.2476Hannan-Quinncriter.15.73334F-statistic309.2239Durbin-Watsonstat1.913390Prob(F-statistic)0.000000由此可見，該模型R2=0.9116圖2-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:19Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-55.22996445.3505-0.1240150.9021X12.5310380.19305013.110760.0000R-squared0.851406Meandependentvar5495.281AdjustedR-squared0.846453S.D.dependentvar1995.508S.E.ofregression781.9424Akaikeinfocriterion16.22190Sumsquaredresid18343018Schwarzcriterion16.31351Loglikelihood-257.5504Hannan-Quinncriter.16.25227F-statistic171.8921Durbin-Watsonstat1.155038Prob(F-statistic)0.000000

由此可見，該模型R2=0.8514圖2-3DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:18Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C1606.443476.08733.3742620.0021X210.464241.1768778.8915330.0000R-squared0.724920Meandependentvar5495.281AdjustedR-squared0.715751S.D.dependentvar1995.508S.E.ofregression1063.905Akaikeinfocriterion16.83774Sumsquaredresid33956823Schwarzcriterion16.92935Loglikelihood-267.4039Hannan-Quinncriter.16.86811F-statistic79.05937Durbin-Watsonstat1.422873Prob(F-statistic)0.000000由此可見，該模型R?=0.7249圖2-4DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:16Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C700.9945425.02831.6492890.1095X34.7462160.39424912.038630.0000R-squared0.828502Meandependentvar5495.281AdjustedR-squared0.822785S.D.dependentvar1995.508S.E.ofregression840.0476Akaikeinfocriterion16.36526Sumsquaredresid21170400Schwarzcriterion16.45686Loglikelihood-259.8441Hannan-Quinncriter.16.39562

F-statisticProb(F-statistic)144.92870.000000Durbin-Watsonstat1.400608由此可見，該模型R2=0.8285圖2-5DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:15Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C1416.370220.41406.4259550.0000X46.9555910.34080920.409050.0000R-squared0.932815Meandependentvar5495.281AdjustedR-squared0.930576S.D.dependentvar1995.508S.E.ofregression525.7865Akaikeinfocriterion15.42813Sumsquaredresid8293544.Schwarzcriterion15.51974Loglikelihood-244.8501Hannan-Quinncriter.15.45849F-statistic416.5292Durbin-Watsonstat1.863327Prob(F-statistic)0.000000由此可見，該模型R2=0.9328由圖2-1、2-2、2-3、2-4、2-5知X4的R?最大所以以以yx4作為基礎(chǔ)再用Y分別對(duì)XX4、X1X4、X2X4、X3X4作線性回歸；結(jié)果如圖圖3-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:32Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C960.5453206.43104.6531060.0001X0.2831880.0668344.2371550.0002X44.1282080.7207495.7276600.0000R-squared0.958504Meandependentvar5495.281AdjustedR-squared0.955643S.D.dependentvar1995.508S.E.ofregression420.2775Akaikeinfocriterion15.00877

Sumsquaredresid5122363.Schwarzcriterion15.14618Loglikelihood-237.1403Hannan-Quinncriter.15.05432F-statistic334.9350Durbin-Watsonstat2.190815Prob(F-statistic)0.000000圖3-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:33Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C625.1550245.52052.5462440.0165X10.9279560.2056684.5119250.0001X44.8346390.5399728.9534930.0000R-squared0.960526Meandependentvar5495.281AdjustedR-squared0.957803S.D.dependentvar1995.508S.E.ofregression409.9148Akaikeinfocriterion14.95884Sumsquaredresid4872875.Schwarzcriterion15.09625Loglikelihood-236.3414Hannan-Quinncriter.15.00438F-statistic352.8259Durbin-Watsonstat2.316899Prob(F-statistic)0.000000圖3-3DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:33Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C1421.820240.08235.9222180.0000X2-0.0799051.260403-0.0633960.9499X46.9969360.7385559.4738130.0000R-squared0.932824Meandependentvar5495.281AdjustedR-squared0.928192S.D.dependentvar1995.508

S.E.ofregression534.7379Akaikeinfocriterion15.49049Sumsquaredresid8292394.Schwarzcriterion15.62790Loglikelihood-244.8478Hannan-Quinncriter.15.53604F-statistic201.3524Durbin-Watsonstat1.872625Prob(F-statistic)0.000000圖3-4DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:33Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C877.1122205.59884.2661340.0002X31.6767130.3608014.6472010.0001X44.9869800.49831310.007720.0000R-squared0.961492Meandependentvar5495.281AdjustedR-squared0.958836S.D.dependentvar1995.508S.E.ofregression404.8647Akaikeinfocriterion14.93404Sumsquaredresid4753548.Schwarzcriterion15.07146Loglikelihood-235.9447Hannan-Quinncriter.14.97959F-statistic362.0467Durbin-Watsonstat1.414208Prob(F-statistic)0.000000由圖可知R2=0.9615最大，則以YX3X4為基礎(chǔ)用Y分別對(duì)XX3X4、X1X3X4、X2X3X4作多元線性回歸結(jié)果如圖圖4-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:38Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.647.4620179.50723.6068860.0012

647.4620179.50723.6068860.0012X0.2133450.0554933.8445210.0006X31.3230200.3109804.2543570.0002X43.2721890.6060335.3993570.0000R-squared0.974796Meandependentvar5495.281AdjustedR-squared0.972096S.D.dependentvar1995.508S.E.ofregression333.3395Akaikeinfocriterion14.57267Sumsquaredresid3111227.Schwarzcriterion14.75589Loglikelihood-229.1627Hannan-Quinncriter.14.63340F-statistic360.9839Durbin-Watsonstat1.470526Prob(F-statistic)0.000000圖4-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:38Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C110.9190151.00290.7345490.4687X10.9102580.1147217.9345630.0000X31.6458320.2037648.0771630.0000X42.9427360.3815087.7134360.0000R-squared0.988146Meandependentvar5495.281AdjustedR-squared0.986876S.D.dependentvar1995.508S.E.ofregression228.6073Akaikeinfocriterion13.81836Sumsquaredresid1463317.Schwarzcriterion14.00157Loglikelihood-217.0937Hannan-Quinncriter.13.87909F-statistic778.0155Durbin-Watsonstat1.865370Prob(F-statistic)0.000000圖4-3DependentVariable:Y

Method:LeastSquaresDate:06/15/14Time:15:39Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C914.0456213.00304.2912340.0002X2-0.7256660.971530-0.7469310.4613X31.7157550.3673214.6710000.0001X45.3166220.6685297.9527140.0000R-squared0.962244Meandependentvar5495.281AdjustedR-squared0.958199S.D.dependentvar1995.508S.E.ofregression407.9865Akaikeinfocriterion14.97681Sumsquaredresid4660683.Schwarzcriterion15.16003Loglikelihood-235.6290Hannan-Quinncriter.15.03754F-statistic237.8710Durbin-Watsonstat1.582733Prob(F-statistic)0.000000由圖可知R2=0.9881最大，則以YX1X3X4為基礎(chǔ)用Y分別對(duì)XX1X3X4、X1X2X3X4作多元線性回歸結(jié)果如圖圖5-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:41Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C125.6046143.06640.8779460.3877X0.0854750.0413652.0663730.0485X10.7835040.1246896.2836610.0000X31.5084280.2039607.3957170.0000X42.5403810.4101746.1934310.0000R-squared0.989765Meandependentvar5495.281AdjustedR-squared0.988248S.D.dependentvar1995.508S.E.ofregression216.3246Akaikeinfocriterion13.73404Sumsquaredresid1263501.Schwarzcriterion13.96306Loglikelihood-214.7446Hannan-Quinncriter.13.80995F-statisticF-statistic652.7227Durbin-Watsonstat1.966234Prob(F-statistic)0.000000Prob(F-statistic)0.000000圖5-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:42Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-165.6481144.2608-1.1482550.2609X21.9654310.5198263.7809390.0008X11.1199860.10954710.223830.0000X31.5329740.1704208.9952750.0000X41.5789130.4783303.3008870.0027R-squared0.992249Meandependentvar5495.281AdjustedR-squared0.991101S.D.dependentvar1995.508S.E.ofregression188.2426Akaikeinfocriterion13.45594Sumsquaredresid956752.1Schwarzcriterion13.68496Loglikelihood-210.2951Hannan-Quinncriter.13.53185F-statistic864.1597Durbin-Watsonstat1.886714Prob(F-statistic)0.0000004、異方差性的檢驗(yàn)White檢驗(yàn)結(jié)果如下:F-statistic3.562028Prob.F(20,11)0.0173Obs*R-squared27.71987Prob.Chi-Square(20)0.1162ScaledexplainedSS20.21128Prob.Chi-Square(20)0.4448TestEquation:DependentVariable:RESIDEMethod:LeastSquaresDate:06/15/14Time:15:59Sample:435Includedobservations:32VariableCoefficientStd.Errort-StatisticProb.C-629464.1234087.0-2.6890180.0211X-120.502761.84256-1.9485400.0773XA20.0048490.0106720.4543730.6584X*X10.0403980.0530730.7611760.4626X*X20.1107580.2181600.5076900.6217X*X3-0.0259560.067862-0.3824840.7094X*X4-0.1027320.207896-0.4941530.6309X1881.4267357.99612.4621130.0316X1A2-0.1830450.131816-1.3886350.1924X1*X2-3.1660960.815849-3.8807400.0026X1*X3-0.3470850.237409-1.4619690.1717X1*X41.8261670.7002162.6080060.0243X24813.7931054.9284.5631480.0008X2A2-9.4608611.743218-5.4272400.0002X2*X3-3.1690440.945138-3.3529940.0064X2*X419.441643.4849345.5787680.0002X395.26270167.21940.5696870.5803X3A20.5241290.2212402.3690490.0372X3*X41.6849551.2594451.3378540.2079X4-2789.149760.8548-3.6658100.0037X4A2-7.5541641.373581-5.4996120.0002R-squared0.866246Meandependentvar29507.75AdjustedR-squared0.623057S.D.dependentvar44557.74S.E.ofregression27356.53Akaikeinfocriterion23.51596Sumsquaredresid8.23E+09Schwarzcriterion24.47785Loglikelihood-355.2553Hannan-Quinncriter.23.83480F-statistic3.562028Durbin-Watsonstat1.928746Prob(F-statistic)0.017282表3由表3可以看出，nR?=27.71987,由White檢驗(yàn)知，在a=0.05下，查X?分布表,得臨界值=30.1435，比較計(jì)算的X彳統(tǒng)計(jì)量與臨界值，因?yàn)閚R2=27.71987V30.1435，表明模型不存在異方差。5、自相關(guān)的檢驗(yàn)通過DW檢驗(yàn)法由表1知該模型的DW統(tǒng)計(jì)量=1.9334查DW分布表可得臨界值dL=1.144dU