计量经济学论文(eviews分析)房价的计量经济分析

1、计量经济学论文eviews分析房价的计量经济分析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.而这场博弈的焦点那么是房价问题.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那么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；最后那么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

2、焦点.而对于房价的构成因素,至今仍然是不透明的.公布房价本钱成为另政府极为头疼的一件事.房价本钱是一个非常复杂的集合体,并且工程间差异性较大,同时还有软资产、品牌等组成局部,特别是现在的商品房,追求品质、功能完善以及个性化本钱构成越来越难衡量.写作目的：通过对一系列影响房价的根本因素的分析,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.当然笔者的水平有限,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：房价本钱计量假设检验

3、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咯地区建筑业总产值.万元X1咯地区房屋竣工面积.万平方米X2咯地区建筑业企业从业人员.人X3咯地区建筑业劳动生产率.元/人X4咯地区人均住宅面积.平方米X5咯地区人均可支配收入.元数据如下：Y126985215208402.7799313.5401279.2576575.10170794X1X3X2X4X54254.856976712996124.77113882.00.0.040621465.823895714

4、706323.09510312.00.0.070914748.398931770048.23.1677239.000.00010601313.359127689151.22.9967005.000.00080301450.726595361074.20.0537012.900.00010003957.196679082496.20.2357240.500.00010803469281626.81.004401872181.38.001195803609.2340027949317730.540031272716183.79906227074017.63.005493442952.11. 003

5、0383777486.00044151868033.000505185153910.0.02727001005696.02429351274302. .091069166407.000553611108288.0.020.4926678.9000029.34514867.304924.4359262.4306031.02313179.305320.7546778.0803030.2989999.570403593352750.957470570826.22.6196901.41481369139.818006345213433.67. 0020725360728.0.0093290166056

6、.00024.4808399.9801020.2006926.190208729954840.88. 008188404969.72. 001516328105.042002818461721.66.00394053121.50.00010487681761.3. 0011191074553.6. 001492821019320.035370077472.00061210.55361.000022.9027321.9808024.4257674.2800024.93212380.804324.1737785.0204023.4327259.200505862094939.65.00122533

7、8784.67400212290980.307. 003967952248.77. 00293427121.30.0004404361580.02.002236861327.20.0081799769432.00020705359748.4.0029331072152.00052247069238.00036593.73205.000041031193212.00044940946857.00025.7248093.6407026.3587041.8507018.1946569.2303024.9297643.5407019.9298765.4905021.7506806.3505021.11

8、36657.28040747325242.9010150161046.19,1056745.3.000.0005020108054578.7088225.61459.22,2556530.46.00000000803196771450.820337595835.20.7817173.54.00.0001040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.X1X2X3X4X5Y0.96087099090.27137519270.53869727900.41830680020.9614738426X1107446

9、607756904195329080420.96087099090.12502937500.47788589150.27985062330.8986725515X207446197319187344358116060.27137519270.12502937500.54088095990.83624084890.4677103837X360775973191699264241600920.53869727900.47788589150.54088095990.68651280850.5897771488X46904118736992610774261270.41830680020.279850

10、62330.83624084890.68651280850.5898233852X5953294435842410774162140.96147384260.89867255150.46771038370.58977714880.5898233852Y0804211606600922612762141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:LeastSquaresDate:12/06/10Time:17:37Sample(adjusted):131Includedobservati

11、ons:31afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.X11651.40387.6770318.835080.0000C903234.0502408.21.7978090.0826R-squared0.924432Meandependentvar7446408.AdjustedR-squared0.921826S.D.dependentvar7227629.S.E.ofregression2021815.Akaikeinfocriterion31.93824Sumsquaredresid1.18E+14Schwarz

12、criterion32.03076Loglikelihood-493.0427F-statistic354.7601Durbin-Watsonstat1.930762Prob(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.通过比拟发现引入X3时,拟合优度最大,所以参加X3DependentVariable:YMethod:LeastSquaresDate:12/06/10Time:17:40Sample(adjusted):131Includedobservations:31afteradjustmentsVariableCoefficie

13、ntStd.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+13Schwarzcrite

14、rion31.19771Loglikelihood-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:LeastSquaresDate:12/06/10Time:17:47Sample(adjusted):131Includedobservations:31afteradjustmentsCoefficient-StatisticProb.VariabletStd.

15、ErrorX11569.18666.7446723.510290.0000X364.0494510.562586.0638100.0000X4-69455.16102797.7-0.6756490.5050C-2476469.1985261.-1.2474280.2230R-squared0.971083Meandependentvar7446408.AdjustedR-squared0.967870S.D.dependentvar7227629.S.E.ofregression1295550.Akaikeinfocriterion31.10668Sumsquaredresid4.53E+13

16、Schwarzcriterion31.29171Loglikelihood-478.1536F-statistic302.2316Durbin-Watsonstat2.298423Prob(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,且拟合度没有显著提升.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-Watsonstat2.0

17、98685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:RESIDA2Method:LeastSquaresDate:12/06/10Time:18:05Sample:131Includedobservations:31Coefficient-StatisticProb.VariabletStd.ErrorC-3.19E+124.46

18、E+12-0.7158550.4807X11.15E+083.54E+080.3249150.7479XW23913.00420466.630.1911890.8499X1*X3-756.30894598.986-0.1644510.8707X369425884952903000.7285720.4730X3A2-184.1939462.0769-0.3986220.6936AdjustedR-squared0.110127S.D.dependentvar2.04E+12S.E.ofregression1.92E+12Akaikeinfocriterion59.58019Sumsquaredr

19、esid9.25E+25Schwarzcriterion59.85774Loglikelihood-917.4929F-statistic1.742532Durbin-Watsonstat2.029951Prob(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WhiteHeteroskedasticityTest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.没方法,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Dep

20、endentVariable:YMethod:LeastSquaresDate:12/06/10Time:18:13Sample(adjusted):131Includedobservations:31afteradjustmentsWeightingseries:1/ABS(RESID)VariableCoefficientStd.Errort-StatisticProb.X11543.8124.266721361.82620.0000X360.882210.92521265.803540.0000C-3721097.59118.40-62.943140.0000WeightedStatis

21、ticsR-squared0.999999Meandependentvar7466651.AdjustedR-squared0.999999S.D.dependentvar34381715S.E.ofregression29817.20Akaikeinfocriterion23.53532Sumsquaredresid2.49E+10Schwarzcriterion23.67410Loglikelihood-361.7975F-statistic310479.3Durbin-Watsonstat2.158638Prob(F-statistic)0.000000UnweightedStatist

22、icsR-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升,且通过再次异方差检验通过了自相关检验Breusch-GodfreySerialCorrelationLMTest:Obs*R-squared0.505922Probability0.776498TestEquation:DependentVariable:RESIDMethod:LeastSquaresDate:12/06/10Time:18:26Presamplemissingvaluelaggedresidualssettozero.VariableCoefficientStd.Errort-StatisticProb.X1-6.77803562.81436-0.1079060.9149X31.2596669.7075420.1297620.8978C-73457.01800910.8-0.0917170.9276RESID(-1)-0.