计量经济学论文eviews分析 房价的计量经济分析

1、房价的计量经济济分析引言：近改革开开放20多年年来，从来没没有哪一个行行业像房地产产业这样盛产产亿万富翁，各各种富豪排行行榜上，房地地产富豪连年年占据半壁江江山；“中国十大暴暴利行业”中，房地产产业每年都是是“第一名”。是什么造造就了这样的的状况。房地地产的问题，在在开发商，政政府，购房者者三者来看，就就是一场完完完全全的博弈弈。而这场博博弈的焦点则则是房价问题题。如果说开开发商与政府府之间的博弈弈是围绕“土地”这个关键词词，那么整个个房地产市场场则在价格上上开展了新一一轮的对峙。先先是开发商与与购房者在房房价涨跌上僵僵持不下；再再有开发商与与政府之间的的土地成本论论；最后则是是关于房地产产是

2、否归为暴暴利行业的争争执，“价格”成了市场关关注的焦点。而而对于房价的的构成因素，至至今仍然是不不透明的。公公布房价成本本成为另政府府极为头疼的的一件事。房房价成本是一一个非常复杂杂的集合体，并并且项目间差差异性较大，同同时还有软资资产、品牌等等组成部分，特特别是现在的的商品房，追追求品质、功功能完善以及及个性化成本本构成越来越越难衡量。 写作目的：通过过对一系列影影响房价的基基本因素的分分析，了解对对其主要因素素和次要因素素。并对这些些因素进行统统计推断和经经济意义上的的检验。选择择拟和效果最最好的最为结结论。在一定定层面上分析析房地产如此此暴利的因素素。当然笔者者的能力有限限，并不能全全面

3、的分析这这一问题。仅仅仅就几个因因素进行分析析。写作方法：理论论分析及计量量分析方法，将将会用到Evviews软软件进行帮助助分析。关键词：房价成成本 计量假设检检验 最小二二乘法 拟拟合优度 现在我们以20003年的数数据，选取330个省市的的数据为例进进行分析。在在Eviewws软件中选选择建立截面面数据。现在在我们以20003年的数数据，选取331个省市的的数据为例进进行分析。令令Y=各地区建建筑业总产值值。（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

4、收入。（元）数据如下：YX1X3X2X4X5 126985521 4254.8800 5697677.0 1299611.0 24.771140 13882.62 52084002. 1465.8800 2389577.0 1470633.0 23.095570 10312.91 77993113. 4748.3300 9893177.0 70048.00 23.167710 7239.0060 54012779. 1313.3300 5912766.0 89151.00 22.996680 7005.0030 25765775. 1450.7700 2659533.0 61074.00 20

5、.053310 7012.9900 101707794 3957.1100 9667900.0 82496.00 20.235510 7240.5580 34692881. 1626.8800 3038377.0 77486.00 20.705590 7005.1170 44018778. 2181.3300 4415188.0 68033.00 20.492200 6678.9900 119580034 3609.2200 5051855.0 1539100.0 29.345530 14867.49 279493354 17730.00 27270006. 1005699.0 24.4355

6、30 9262.4460 312727779 16183.90 24293552. 1274300.0 31.023330 13179.53 62270773. 4017.6600 9106911.0 66407.00 20.754480 6778.0030 54934441. 2952.1100 5536111.0 1082888.0 30.298870 9999.5540 35933556. 2750.9900 5747055.0 70826.00 22.619980 6901.4420 148136618 9139.8800 20725330. 60728.00 24.480080 83

7、99.9910 63452117. 3433.6600 9329011.0 66056.00 20.200090 6926.1120 87299558. 4840.8800 10487663. 81761.00 22.902280 7321.9980 81884002. 4969.7700 11191006. 74553.00 24.425580 7674.2200 151632242 8105.0000 14928220. 1019322.0 24.932280 12380.43 28184666. 1721.6600 3537000.0 77472.00 24.173320 7785.00

8、40 3940533.0 121.50000 61210.00 55361.00 23.432200 7259.2250 58620995. 4939.6600 8179977.0 69432.00 25.724440 8093.6670 122533374 8784.6600 20705334. 59748.00 26.358850 7041.8870 21229007. 980.30000 2933100.0 72152.00 18.194430 6569.2230 39679557. 2248.7700 5224700.0 69238.00 24.929940 7643.5570 293

9、4277.0 121.30000 36593.00 73205.00 19.929990 8765.4450 44043662. 1580.0000 4103111.0 93212.00 21.750050 6806.3350 22368660. 1327.2200 4494099.0 46857.00 21.113380 6657.2240 7473255.0 242.90000 1015011.0 61046.00 19.105550 6745.3320 10805446. 578.70000 88225.00 61459.00 22.255500 6530.4480 31967774.

10、1450.8800 2033755.0 95835.00 20.781110 7173.5540 做多重共线性检检验：引入的变量太多多，可能存在在变量间的共共线性，影响响方程的估计计。首先进行行做多重共线线性检验可以以减少变量使使后面的分析析变得简洁。X1X2X3X4X5YX110.9608770990990744660.2713775192776077550.5386997279006904110.4183006800229532990.961477384266080422X20.96087709909907446610.1250229375009731990.477888589155187

11、30.2798550623334435880.898677255155116066X30.2713775192776077550.12502293750097319910.5408880959996992660.83624408489942410.467711038377600922X40.5386997279006904110.47788858915518730.54088809599969926610.68651128085507740.589777714888261277X50.4183006800229532990.2798550623334435880.836244084899424

12、10.686511280855077410.5898223385226214Y0.9614773842660804220.8986772551551160660.4677110383776009220.5897777148882612770.58982233852262141可以看出有多重重共线性。采取逐步回归法法：第一次回归，我我们可以根据据T检验值和和可决系数看看出：X1的的效果最好：Dependeent Vaariablle: YMethod: Leasst SquuaresDate: 112/06/10 Time: 17:337Sample (adjuusted): 1 331Incl

13、udeed obsservattions: 31 aafter adjusstmenttsVariablleCoefficcientStd. Errrort-StatiisticProb.X11651.400387.67700318.8350080.0000C903234.0502408.21.79780090.0826R-squarred0.9244332Meaan deppendennt varr74464088.Adjusteed R-ssquareed0.9218226S.DD. deppendennt varr72276299.S.E. off regrressioon2020815

14、5.Akaaike iinfo ccriterrion31.938224Sum squuared residd1.18E+114Schhwarz criteerion32.030776Log likkelihoood-493.04427F-sstatisstic354.76001Durbin-Watsoon staat1.9307662Proob(F-sstatisstic)0.0000000而X1于X2存存在严重自相相关，所以引引入第二个变变量时将X22排除。通过过比较发现引引入X3时，拟拟合优度最大大，所以加入入X3Dependeent Vaariablle: YMethod: Leass

15、t SquuaresDate: 112/06/10 Time: 17:440Sample (adjuusted): 1 331Includeed obsservattions: 31 aafter adjusstmenttsVariablleCoefficcientStd. Errrort-StatiisticProb.X11547.355457.83199726.7560040.0000X360.5757779.13689996.62979950.0000C-37118880.765709.2-4.84766370.0000R-squarred0.9705994Meaan deppenden

16、nt varr74464088.Adjusteed R-ssquareed0.9684993S.DD. deppendennt varr72276299.S.E. off regrressioon12829144.Akaaike iinfo ccriterrion31.058993Sum squuared residd4.61E+113Schhwarz criteerion31.197771Log likkelihoood-478.41134F-sstatisstic462.08886Durbin-Watsoon staat2.0986885Proob(F-sstatisstic)0.0000

17、000 X3与X5也也存在严重共共线性，在引引入第三个变变量时同时排排除X5，那那只能引入XX4了Dependeent Vaariablle: YMethod: Leasst SquuaresDate: 112/06/10 Time: 17:447Sample (adjuusted): 1 331Includeed obsservattions: 31 aafter adjusstmenttsVariablleCoefficcientStd. Errrort-StatiisticProb.X11569.188666.74466723.5102290.0000X364.04944510.5625

18、586.06381100.0000X4-69455.16102797.7-0.67566490.5050C-24764669.19852611.-1.24744280.2230R-squarred0.9710883Meaan deppendennt varr74464088.Adjusteed R-ssquareed0.9678770S.DD. deppendennt varr72276299.S.E. off regrressioon12955500.Akaaike iinfo ccriterrion31.106668Sum squuared residd4.53E+113Schhwarz

19、criteerion31.291771Log likkelihoood-478.15536F-sstatisstic302.23116Durbin-Watsoon staat2.2984223Proob(F-sstatisstic)0.0000000但是引入后通过过T检验X4不显著，同同时常数项CC也变得不显显著，且拟合合度没有显著著提高。所以剔除X44。通过该检检验最终模型型为：Y = 15447.3544325*XX1 + 660.5755766444*X3 - 37111880.1158T= 26.775604 6.6297995 -4.8447637F-sstatisstic354.7

20、6001R-squarred0.9705994Durbin-Watsoon staat2.0986885 以上指标都显显示拟合得很很好。 异方差检验White HHeterooskedaasticiity Teest:F-statiistic1.7425332Proobabillity0.1616997Obs*R-ssquareed8.0116002Proobabillity0.1555997Test Eqquatioon:Dependeent Vaariablle: REESID22Method: Leasst SquuaresDate: 112/06/10 Time: 18:005Samp

21、le: 1 311Includeed obsservattions: 31VariablleCoefficcientStd. Errrort-StatiisticProb.C-3.19E+124.46E+112-0.71588550.4807X11.15E+0083.54E+0080.32491150.7479X123913.000420466.6630.19118890.8499X1*X3-756.300894598.9886-0.16444510.8707X36942588849529030000.72857720.4730X32-184.19939462.07669-0.39866220

22、.6936R-squarred0.2584339Meaan deppendennt varr1.49E+112Adjusteed R-ssquareed0.1101227S.DD. deppendennt varr2.04E+112S.E. off regrressioon1.92E+112Akaaike iinfo ccriterrion59.580119Sum squuared residd9.25E+225Schhwarz criteerion59.857774Log likkelihoood-917.49929F-sstatisstic1.7425332Durbin-Watsoon s

23、taat2.0299551Proob(F-sstatisstic)0.1616997从结果来看应该该勉强是不存存在异方差的的，但是同方方差的概率有有点小，不能能让人信服。而而通过残差图图发现残差没没有很明显的的波动、X-Y的图也较较符合线性关关系即模型设设定没多大问问题、且从WWhite Heterroskeddasticcity TTest 中中各变量的系系数也十分不不显著不能判判别残差是否否与解释变量量有关。没办办法，只能用用加权最小二二乘法进行修修正。异方差修正-加权最小小二乘法 Dependeent Vaariablle: YMethod: Leasst SquuaresDate: 1

24、12/06/10 Time: 18:113Sample (adjuusted): 1 331Includeed obsservattions: 31 aafter adjusstmenttsWeightiing seeries: 1/ABBS(RESSID)VariablleCoefficcientStd. Errrort-StatiisticProb.X11543.81124.2667221361.826620.0000X360.8822210.925211265.8035540.0000C-37210997.59118.440-62.9433140.0000Weighteed Staati

25、stiicsR-squarred0.9999999Meaan deppendennt varr74666511.Adjusteed R-ssquareed0.9999999S.DD. deppendennt varr343817115S.E. off regrressioon29817.220Akaaike iinfo ccriterrion23.535332Sum squuared residd2.49E+110Schhwarz criteerion23.674110Log likkelihoood-361.79975F-sstatisstic310479.3Durbin-Watsoon s

26、taat2.1586338Proob(F-sstatisstic)0.0000000Unweighhted SStatissticsR-squarred0.9705889Meaan deppendennt varr74464088.Adjusteed R-ssquareed0.9684889S.DD. deppendennt varr72276299.S.E. off regrressioon12830099.Summ squaared rresid4.61E+113Durbin-Watsoon staat2.0999000通过修正以后拟拟合度有所提提高，且通过过再次异方差差检验通过了了。自相

27、关检验Breuschh-Godffrey SSeriall Corrrelatiion LMM Testt:Obs*R-ssquareed0.5059222Proobabillity0.7764998Test Eqquatioon:Dependeent Vaariablle: REESIDMethod: Leasst SquuaresDate: 112/06/10 Time: 18:226Presampple miissingg valuue laggged rresiduuals sset too zeroo.VariablleCoefficcientStd. Errrort-StatiisticProb.X1-6.778003562.814336-0.10799060.9149X31.25966669.70754420.12976620.8978C-73457.01800910.8-0.09177170.9276RESID(-1