计量经济学论文(eviews分析) 房价的计量经济分析

……………最新资料推荐………………最新资料推荐…………………房价的计量经济分析20豪排行榜上，房地产富豪连年占据半壁江山“于房地产是否归为暴利行业的争执至个因素进行分析。写作方法：理论分析及计量分析方法，将会用到Eviews软件进行帮助分析。关键词：房价成本计量假设检验最小二乘法拟合优度现在我们以2003年的数据，选取30个省市的数据为例进行分析。在Eviews软件中选择建立截面数据。现在我们以2003年的数据，选取31个省市的数据为例进行分析。令Y=各地区建筑业总产值。（万元）X1=各地区房屋竣工面积。（万平方米）X2=各地区建筑业企业从业人员。（人）X3=各地区建筑业劳动生产率。（元/人）X4=各地区人均住宅面积。（平方米）X5=各地区人均可支配收入。（元）数据如下：YX1X3X2X4X5126985214254.800569767.0129961.024.7714013882.625208402.1465.800238957.0147063.023.0957010312.917799313.4748.300989317.070048.0023.167107239.0605401279.1313.300591276.089151.0022.996807005.0302576575.1450.700265953.061074.0020.053107012.900101707943957.100966790.082496.0020.235107240.5803469281.1626.800303837.077486.0020.705907005.1704401878.2181.300441518.068033.0020.492006678.900119580343609.200505185.0153910.029.3453014867.492794935417730.002727006.100569.024.435309262.4603127277916183.902429352.127430.031.0233013179.536227073.4017.600910691.066407.0020.754806778.0305493441.2952.100553611.0108288.030.298709999.5403593356.2750.900574705.070826.0022.619806901.420148136189139.8002072530.60728.0024.480808399.9106345217.3433.600932901.066056.0020.200906926.1208729958.4840.8001048763.81761.0022.902807321.9808188402.4969.7001119106.74553.0024.425807674.200151632428105.0001492820.101932.024.9328012380.432818466.1721.600353700.077472.0024.173207785.040394053.0121.500061210.0055361.0023.432007259.2505862095.4939.600817997.069432.0025.724408093.670122533748784.6002070534.59748.0026.358507041.8702122907.980.3000293310.072152.0018.194306569.2303967957.2248.700522470.069238.0024.929407643.570293427.0121.300036593.0073205.0019.929908765.4504404362.1580.000410311.093212.0021.750506806.3502236860.1327.200449409.046857.0021.113806657.240747325.0242.9000101501.061046.0019.105506745.3201080546.578.700088225.0061459.0022.255006530.4803196774.1450.800203375.095835.0020.781107173.540做多重共线性检验 ：可以减少变量使后面的分析变得简洁。X1X2X3X4X5Y0.96087099090.27137519270.53869727900.41830680020.9614738426X1107446607756904195329080420.96087099090.12502937500.47788589150.27985062330.8986725515X207446197319187344358116060.27137519270.12502937500.54088095990.83624084890.4677103837X360775973191699264241600920.53869727900.47788589150.54088095990.68651280850.5897771488X46904118736992610774261270.41830680020.27985062330.83624084890.68651280850.5898233852X5953294435842410774162140.96147384260.89867255150.46771038370.58977714880.5898233852Y0804211606600922612762141可以看出有多重共线性。采取逐步回归法：第一次回归，我们可以根据T检验值和可决系数看出：X1的效果最好：DependentVariable:YMethod:LeastDate:12/06/10 Time:Sample(adjusted):131Includedobservations:31afteradjustmentsVariableX1C

Coefficient1651.403903234.0

Std.87.67703502408.2

t-Statistic18.835081.797809

Prob.0.00000.0826R-squared0.924432Meandependentvar7446408.AdjustedR-squared0.921826S.D.dependentvar7227629.S.E.ofregression2020815.Akaikeinfocriterion31.93824Sumsquaredresid1.18E+14Schwarzcriterion32.03076Loglikelihood-493.0427F-statistic354.7601Durbin-Watsonstat1.930762Prob(F-statistic)0.000000X1X2X2X3拟合优度最大，所以加入X3DependentVariable:YMethod:LeastDate:12/06/10 Time:Sample(adjusted):131Includedobservations:31afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.X11547.35457.8319726.756040.0000X360.575779.1368996.6297950.0000C-3711880.765709.2-4.8476370.0000R-squared0.970594Meandependentvar7446408.AdjustedR-squared0.968493S.D.dependentvar7227629.S.E.ofregression1282914.Akaikeinfocriterion31.05893Sumsquaredresid4.61E+13Schwarzcriterion31.19771Loglikelihood-478.4134F-statistic462.0886Durbin-Watsonstat2.098685Prob(F-statistic)0.000000X3与X5也存在严重共线性，在引入第三个变量时同时排除X5，那只能引入X4了DependentVariable:YMethod:LeastDate:12/06/10 Time:Sample(adjusted):131Includedobservations:31afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.X11569.18666.7446723.510290.0000X364.0494510.562586.0638100.0000X4-69455.16102797.7-0.6756490.5050C-2476469.1985261.-1.2474280.2230R-squared0.971083Meandependentvar7446408.AdjustedR-squared0.967870S.D.dependentvar7227629.S.E.ofregression1295550.Akaikeinfocriterion31.10668Sumsquaredresid4.53E+13Schwarzcriterion31.29171Loglikelihood-478.1536F-statistic302.2316Durbin-Watsonstat2.298423Prob(F-statistic)0.000000但是引入后通过T检验X4不显著，同时常数项C也变得不显著，且拟合度没有显著提高。所以剔除X4。通过该检验最终模型为：Y=1547.354325*X1+60.57576644*X3-3711880.158T=26.756046.629795-4.847637F-statistic354.7601R-squared0.970594Durbin-Watsonstat 2.098685以上指标都显示拟合得很好。异方差检验WhiteHeteroskedasticityTest:F-statistic1.742532Probability0.161697Obs*R-squared8.011602Probability0.155597TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:12/06/10 Time:Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C-3.19E+124.46E+12-0.7158550.4807X11.15E+083.54E+080.3249150.7479X1^23913.00420466.630.1911890.8499X1*X3-756.30894598.986-0.1644510.8707X369425884952903000.7285720.4730X3^2-184.1939462.0769-0.3986220.6936R-squared 0.258439 Meandependentvar 1.49E+12AdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat

0.110127S.D.dependentvar1.92E+12Akaikeinfocriterion9.25E+25Schwarzcriterion-917.4929F-statistic2.029951Prob(F-statistic)

2.04E+1259.5801959.857741.7425320.161697X-Y的图也较符合线性关系即模型设定没多大问题、且从HeteroskedasticityTest法，只能用加权最小二乘法进行修正。异方差修正---加权最小二乘法DependentVariable:YMethod:LeastDate:12/06/10 Time:Sample(adjusted):131Includedobservations:31afteradjustmentsWeightingseries:1/ABS(RESID)VariableCoefficientStd.Errort-StatisticProb.X11543.8124.266721361.82620.0000X360.882210.92521265.803540.0000C-3721097.59118.40-62.943140.0000WeightedStatisticsR-squared0.999999Meandependentvar7466651.AdjustedR-squared0.999999S.D.dependentvar34381715S.E.ofregression29817.20Akaikeinfocriterion23.53532Sumsquaredresid2.49E+10Schwarzcriterion23.67410Loglikelihood-361.7975F-statistic310479.3Durbin-Watsonstat2.158638Prob(F-statistic)0.000000UnweightedStatisticsR-squared0.970589Meandependentvar7446408.AdjustedR-squared0.968489S.D.dependentvar7227629.S.E.ofregression1283009.Sumsquaredresid4.61E+13Durbin-Watsonstat2.099900通过修正以后拟合度有所提高，且通过再次异方差检验通过了。自相关检验Breusch-GodfreySerialCorrelationLMTest:Obs*R-squared 0.505922 Probability 0.776498TestEquation:DependentVariable:RESIDMethod:LeastSquaresDate:12/06/10 Time:18:26Presamplemissingv