计量经济学论文(eviews分析)房价的计量经济分析.doc

1、房价的计量经济分析引言：近改革开放20 多年来，从来没有哪一个行业像房地产业这样盛产亿万富翁，各种富豪排行榜上，房地产富豪连年占据半壁江山； “中国十大暴利行业”中，房地产业每年都是“第一名” 。是什么造就了这样的状况。房地产的问题，在开发商，政府，购房者三者来看，就是一场完完全全的博弈。 而这场博弈的焦点则是房价问题。 如果说开发商与政府之间的博弈是围绕“土地”这个关键词， 那么整个房地产市场则在价格上开展了新一轮的对峙。 先是开发商与购房者在房价涨跌上僵持不下； 再有开发商与政府之间的土地成本论； 最后则是关于房地产是否归为暴利行业的争执， “价格” 成了市场关注的焦点。 而对于房价的构成

2、因素，至今仍然是不透明的。 公布房价成本成为另政府极为头疼的一件事。 房价成本是一个非常复杂的集合体，并且项目间差异性较大，同时还有软资产、 品牌等组成部分， 特别是现在的商品房，追求品质、功能完善以及个性化成本构成越来越难衡量。写作目的： 通过对一系列影响房价的基本因素的分析， 了解对其主要因素和次要因素。 并对这些因素进行统计推断和经济意义上的检验。 选择拟和效果最好的最为结论。 在一定层面上分析房地产如此暴利的因素。 当然笔者的能力有限， 并不能全面的分析这一问题。 仅仅就几个因素进行分析。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

3、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哈地区建筑业 总产值。（万元） X1哈地区房屋竣工面积。（万平方米） X2咯地区建筑业企业从业人员。（人）X3*地区建筑业劳动生产率。（元 /人）X4哈地区人均住宅面积。（平方米）X5=各地区人均可支配收入。（元）数据如下：YX1X3X2X4X5126985214254.800569767.0129961.024.7714013882.625208402.1465.800238957.0147063.0

4、23.0957010312.917799313.4748.300989317.070048.0023.167107239.0605401279.1313.300591276.089151.0022.996807005.0302576575.1450.700265953.061074.0020.053107012.900101707943957.100966790.082496.0020.235107240.5803469281.1626.800303837.077486.0020.705907005.1704401878.2181.300441518.068033.0020.492006678

5、.900119580343609.200505185.0153910.029.3453014867.492794935417730.002727006.100569.024.435309262.4603127277916183.902429352.127430.031.0233013179.536227073.4017.600910691.066407.0020.754806778.0305493441.2952.100553611.0108288.030.298709999.5403593356.2750.900574705.070826.0022.619806901.42014813618

6、9139.8002072530.60728.0024.480808399.9106345217.3433.600932901.066056.0020.200906926.1208729958.4840.8001048763.81761.0022.902807321.9808188402.4969.7001119106.74553.0024.425807674.200151632428105.0001492820.101932.024.9328012380.432818466.1721.600353700.077472.0024.173207785.040394053.0121.50006121

7、0.0055361.0023.432007259.2505862095.4939.600817997.069432.0025.724408093.670122533748784.6002070534.59748.0026.358507041.8702122907.980.3000293310.072152.0018.194306569.2303967957.2248.700522470.069238.0024.929407643.570293427.0121.300036593.0073205.0019.929908765.4504404362.1580.000410311.093212.00

8、21.750506806.3502236860.1327.200449409.046857.0021.113806657.240747325.0242.9000101501.061046.0019.105506745.3201080546.578.700088225.0061459.0022.255006530.4803196774.1450.800203375.095835.0020.781107173.540做多重共线性检验引入的变量太多， 可能存在变量间的共线性， 影响方程的估计。 首先进行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

9、.27137519270.53869727900.41830680020.9614738426X1107446607756904195329080420.96087099090.12502937500.47788589150.27985062330.8986725515X207446197319187344358116060.27137519270.12502937500.54088095990.83624084890.4677103837X360775973191699264241600920.53869727900.47788589150.54088095990.68651280850.5

10、897771488X46904118736992610774261270.41830680020.27985062330.83624084890.68651280850.5898233852X5953294435842410774162140.96147384260.89867255150.46771038370.58977714880.5898233852Y0804211606600922612762141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:Dependent Variable: YMethod: Least SquaresD

11、ate: 12/06/10 Time: 17:37Sample (adjusted): 1 31Included observations: 31 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X11651.40387.6770318.835080.0000C903234.0502408.21.7978090.0826R-squared0.924432Mean dependent var7446408.Adjusted R-squared0.921826S.D. dependent var7227629.S.E. o

12、f regression2020815.Akaike info criterion31.93824Sum squared resid1.18E+14Schwarz criterion32.03076Log likelihood-493.0427F-statistic354.7601Durbin-Watson stat1.930762Prob(F-statistic)0.000000而X1于X2存在严重自相关，所以引入第二个变量时将X2排除。通过比较发现引入X3时,拟合优度最大，所以加入X3Dependent Variable: YMethod: Least SquaresDate: 12/06

13、/10 Time: 17:40Sample (adjusted): 1 31Included observations: 31 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X11547.35457.8319726.756040.0000X360.575779.1368996.6297950.0000C-3711880.765709.2-4.8476370.0000R-squared0.970594Mean dependent var7446408.Adjusted R-squared0.968493S.D. dep

14、endent var7227629.S.E. of regression1282914.Akaike info criterion31.05893Sum squared resid4.61E+13Schwarz criterion31.19771Log likelihood-478.4134F-statistic462.0886Durbin-Watson stat2.098685Prob(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Dependent Variable: 丫Method: Least SquaresDat

15、e: 12/06/10 Time: 17:47Sample (adjusted): 1 31Included observations: 31 after adjustmentsVariableCoefficient Std. Error t-Statistic Prob.X11569.18666.7446723.510290.0000X364.0494510.562586.0638100.0000X4-69455.16102797.7-0.6756490.5050C-2476469.1985261.-1.2474280.2230R-squared0.971083Mean dependent

16、var7446408.Adjusted R-squared0.967870S.D. dependent var7227629.S.E. of regression1295550.Akaike info criterion31.10668Sum squared resid4.53E+13Schwarz criterion31.29171Log likelihood-478.1536F-statistic302.2316Durbin-Watson stat2.298423Prob(F-statistic)0.000000但是引入后通过 T检号级X4不显著，同时常数项 C也变得不显著，且拟合度没有显

17、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-statistic 354.7601 R-squared0.970594Durbin-Watson stat2.098685以上指标都显示拟合得很好。异方差检验White Heteroskedasticity Test:F-statistic1.742532Probability0.161697Obs*R-squared8.011602Probability0.155597Test Equati

18、on:Dependent Variable: RESIDA2Method: Least SquaresDate: 12/06/10 Time: 18:05Sample: 1 31Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C-3.19E+124.46E+12-0.7158550.4807X11.15E+083.54E+080.3249150.7479X1A23913.00420466.630.1911890.8499X1*X3-756.30894598.986-0.1644510.8707X3694

19、25884952903000.7285720.4730X3A2-184.1939462.0769-0.3986220.6936R-squared0.258439Mean dependent var1.49E+12Adjusted R-squared0.110127S.D. dependent var2.04E+12S.E. of regression1.92E+12Akaike info criterion59.58019Sum squared resid9.25E+25Schwarz criterion59.85774Log likelihood-917.4929F-statistic1.7

20、42532Durbin-Watson stat2.029951Prob(F-statistic)0.161697从结果来看应该勉强是不存在异方差的，但是同方差的概率有点小， 不能让人信服。而通过残差图发现残差没有很明显的波动、X-Y的图也较符合线性关系即模型设定没多大问题、且从 WhiteHeteroskedasticity Test 中各变量的系数也十分不显著不能判别残差是否与解释变量有关。没办法，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Dependent Variable: YMethod: Least SquaresDate: 12/06/10 Time: 18:13Sa

21、mple (adjusted): 1 31Included observations: 31 after adjustmentsWeighting series: 1/ABS(RESID)VariableCoefficientStd. Errort-StatisticProb.X11543.8124.266721361.82620.0000X360.882210.92521265.803540.0000C-3721097.59118.40-62.943140.0000Weighted StatisticsR-squared0.999999Mean dependent var7466651.Ad

22、justed R-squared0.999999S.D. dependent var34381715S.E. of regression29817.20Akaike info criterion23.53532Sum squared resid2.49E+10Schwarz criterion23.67410Log likelihood-361.7975F-statistic310479.3Durbin-Watson stat2.158638Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.970589Mean dependent

23、 var7446408.Adjusted R-squared0.968489S.D. dependent var7227629.S.E. of regression1283009.Sum squared resid4.61E+13Durbin-Watson stat2.099900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-Godfrey Serial Correlation LM Test:Obs*R-squared0.505922 Probability0.776498Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 12/06/10 Time: 18:26Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb.X1-6.77803562.81436-0.1079060.9149X31.2596669.7075420.1297620.8978C-73457.01800910.8-0.0917170.9276RESID(-1