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房地产影响因素分析房___影响因素分析(背景)xx年以来,我国商品房销售额大幅攀升带动了房______和城市基础设施投资的新一轮高速增长通过产业链的传递,进而又拉动钢材、有色金属、建材、石化等生产资料___的快速上涨,___这些生产资料部门产能投资的成倍扩张,最后导致全社会固定资产投资规模过大、增速过快情况的出现房价过快上涨在推动投资增长过快的`同时,已经成为抑制消费的重要因素房______本身呈自然上涨趋势,房价中___趋势总是看涨随着我国经济发展,居民可支配收入提高,民间资金雄厚,大量资金需要寻找投资渠道,而股票市场等投资渠道目前又处于低迷状态,这是房___投资需求不断扩大的经济背景强劲的CPI上涨说明当前的房价上涨并非孤立,是有其宏观经济背景的宏观调控能否有效防止局部行业过热出现反弹,其中的关键就是要继续加强和完善对房___业的调控(引言)国际上关于房___有一种普遍的观点人均收入超过1000美元,房___市场呈现高速发展阶段欧美等发达国家基本都经历了这样一个阶段我们这篇论文,主要探讨房___影响因素分析,主要从人均收入对房______发展的影响阐述年份X1X2X3Y
19902551.
7361510.
16222704.
331919911111.
2361700.
6233.
3786.
19351992590.
59982026.
6253.
4994.655519932___
7.
0192577.
4294.
21291.
45619943532.
4713496.
2367.
81408.
63919953983.
0814282.
95429.
61590.
86319964071.
1814838.
9467.
41806.
39919973527.
5365160.
3481.
91997.
16119982966.
0575425.
14792062.
56919992818.
8055854472.
82052.
620002674.
2646279.
98476.
62111.617xx
2830.
6886859.
6479.
92169.719xx
2906.
167702.
8475.
12250.177xx
3011.
4248472.
2479.
42359.499xx
3441.
629421.
6495.
22713.878X1=建材成本(元/平方米)X2=居民人均收入(元)X3=物价指数Y=房______(元/平方米)初定模型Y=c+a1*x1+a2*x2+a3*x3+etDependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:04Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
32.
5375780.
5904224.
2979080.0013X
20.
1464950.
0209686.
9865680.0000X1-
0.
0180160.035019-
0.
5144470.6171C
33.
20929118.
27470.
2807810.7841R-squared
0.983094Meandependentvar
1753.317AdjustedR-squared
0.978483S.D.dependentvar
600.9536S.E.ofregression
88.15143Akaikeinfocriterion
12.01917Sumsquaredresid
85477.42Schwarzcriterion
12.20798Loglikelihood-
86.14376F-statistic
213.2186Durbin-Watsonstat
1.504263ProbF-statistic
0.000000一多元线性回归DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:05Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
10.
3360100.
1510842.
2239990.0445C
792.
0169453.
44601.
7466620.1043R-squared
0.275612Meandependentvar
1753.317AdjustedR-squared
0.2198___S.D.dependentvar
600.9536S.E.ofregression
530.7855Akaikeinfocriterion
15.51016Sumsquaredresid
3662533.Schwarzcriterion
15.60457Loglikelihood-___.3262F-statistic
4.946171Durbin-Watsonstat
0.275870ProbF-statistic
0.044490DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:09Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
35.
5017790.
52507510.
478090.0000C-___.
8605220.1227-
2.
2117690.0455R-squared
0.___4128Meandependentvar
1753.317AdjustedR-squared
0.885984S.D.dependentvar
600.9536S.E.ofregression
202.9191Akaikeinfocriterion
13.58706Sumsquaredresid
535290.2Schwarzcriterion
13.68146Loglikelihood-
99.90293F-statistic
109.7903Durbin-Watsonstat
0.440527ProbF-statistic
0.000000DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:10Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
20.
2363470.
01587914.
884170.0000C
561.
997588.
563336.
3457130.0000R-squared
0.944572Meandependentvar
1753.317AdjustedR-squared
0.940308S.D.dependentvar
600.9536S.E.ofregression
146.8243Akaikeinfocriterion
12.93992Sumsquaredresid
280245.9Schwarzcriterion
13.03432Loglikelihood-
95.04937F-statistic
221.5384Durbin-Watsonstat
0.475648ProbF-statistic
0.000000DependentVariable:YMethod:LeastSquaresDate:06/07/05Time:21:42Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
32.
3558330.
4583405.
1399230.0002X
20.
1500860.
0191577.
8347140.0000C
37.56794___.
29910.
3286810.7481R-squared
0.982687Meandependentvar
1753.317AdjustedR-squared
0.979802S.D.dependentvar
600.9536S.E.ofregression
85.40783Akaikeinfocriterion
11.90961Sumsquaredresid
87533.98Schwarzcriterion
12.05122Loglikelihood-
86.32207F-statistic
340.5649Durbin-Watsonstat
1.408298ProbF-statistic
0.000000得到结果发现,x1的系数小,然后对y与x1回归可决系数小,相关性差,剔出这个因素因为___更多取决于供需关系修正之后为Y=c+a2*x2+a3*x3+et二多重线性分析三个表如上X2与X3存在多重共线性,
1.
0000000.
8760730.
8760731.000000DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:09Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
35.
5017790.
52507510.
478090.0000C-___.
8605220.1227-
2.
2117690.0455R-squared
0.___4128Meandependentvar
1753.317AdjustedR-squared
0.885984S.D.dependentvar
600.9536S.E.ofregression
202.9191Akaikeinfocriterion
13.58706Sumsquaredresid
535290.2Schwarzcriterion
13.68146Loglikelihood-
99.90293F-statistic
109.7903Durbin-Watsonstat
0.440527ProbF-statistic
0.000000Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.X
20.
2363470.
01587914.
884170.0000C
561.
997588.
563336.
3457130.0000R-squared
0.944572Meandependentvar
1753.317AdjustedR-squared
0.940308S.D.dependentvar
600.9536S.E.ofregression
146.8243Akaikeinfocriterion
12.93992Sumsquaredresid
280245.9Schwarzcriterion
13.03432Loglikelihood-
95.04937F-statistic
221.5384Durbin-Watsonstat
0.475648ProbF-statistic
0.000000由于引入物价指数改善小,所以模型仅一步改进为Y=c+a2*x2+et三异方差检验ARCHTest:F-statistic
1.315031Probability
0.335173Obs*R-squared
3.963227Probability
0.265462TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:06/05/05Time:23:46Sampleadjusted:1993xxIncludedobservations:12afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C
22737.
9410296.
612.
2082950.0582RESID^2-
10.
2419520.38___
40.6___
930.5453RESID^2-2-
0.
3277690.404787-
0.
8097340.4415RESID^2-3-
0.
2737200.378355-
0.
7234490.4900R-squared
0.330269Meandependentvar
16705.23AdjustedR-squared
0.079120S.D.dependentvar
18205.33S.E.ofregression
17470.29Akaikeinfocriterion
22.63559Sumsquaredresid
2.44E+09Schwarzcriterion
22.79723Loglikelihood-
131.8136F-statistic
1.315031Durbin-Watsonstat
1.842435ProbF-statistic
0.335173ARCH=3.963<临界值7.81473所以无异方差WhiteHeteroskedasticityTest:F-statistic
0.159291Probability
0.854522Obs*R-squared
0.387928Probability
0.823687TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:06/05/05Time:23:46Sample:1990xxIncludedobservations:15VariableCoefficientStd.Errort-StatisticProb.C
31063.
2822612.
201.
3737400.1946X2-
5.
0557549.640127-
0.
5244490.6095X2^
20.
0004210.
0009070.
4646050.6505R-squared
0.02___2Meandependentvar
18683.06AdjustedR-squared-
0.136494S.D.dependentvar
18673.13S.E.ofregression
19906.77Akaikeinfocriterion
22.81236Sumsquaredresid
4.76E+09Schwarzcriterion
22.95397Loglikelihood-
168.0927F-statistic
0.159291Durbin-Watsonstat
1.357657ProbF-statistic
0.854522WHITE=0.3879<临界值7.81473无异方差四自相关分析DW=0.4756查表的dl=1.077du=1.361存在自相关广义差分法修正ρ=1-0.4756/2=0.7622DependentVariable:DYMethod:LeastSquaresDate:06/06/05Time:00:18Sampleadjusted:1991xxIncludedobservations:14afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.DX
20.
1820860.
0349185.
2146550.0002C
236.55___
63.
273883.7___
500.0028R-squared
0.693820Meandependentvar
544.1620AdjustedR-squared
0.668305S.D.dependentvar
148.7133S.E.ofregression
85.64840Akaikeinfocriterion
11.86994Sumsquaredresid
88027.77Schwarzcriterion
11.96124Loglikelihood-
81.0___59F-statistic
27.19263Durbin-Watsonstat
1.584278ProbF-statistic
0.000217得出回归后可决系数降低,考虑其他方法1.迭代法表发现可决系数提高,F统计量提高,DW=1.5547〉1.361已经无自相关结论Y-bY(-1)=c*(1-b)+a2*(x2-b*x2(-1))+et由下表的b=
0.681C=
561.9975a2=
0.
236347179.2772Y*=Y-
0.681Y(-1)X*=x2-
0.681*x2(-1)Y*=
179.2272+
0.2363X*+etMethod:LeastSquaresDate:06/07/05Time:20:57Sampleadjusted:1991xxIncludedobservations:14afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.E
20.
6805090.
1776963.
8296240.0024C
11.
6877324.
888250.
4696080.6471R-squared
0.5499___Meandependentvar
15.32764AdjustedR-squared
0.512488S.D.dependentvar
133.2751S.E.ofregression
93.05539Akaikeinfocriterion
12.03583Sumsquaredresid
103911.7Schwarzcriterion
12.12712Loglikelihood-
82.25081F-statistic
14.66602Durbin-Watsonstat
1.313042ProbF-statistic
0.0023972.改进模型方程(对数法,然后用迭代法)Ly-bLy(-1)=c*(1-b)+a2*(Lx2-b*Lx2(-1)可决系数很高,F统计量相对1中也有提高,DW=
1.
811.361无自相关DependentVariable:LYMethod:LeastSquaresDate:06/06/05Time:10:24Sampleadjusted:1991xxIncludedobservations:14afteradjustingendpointsConvergen___achievedafter7iterationsVariableCoefficientStd.Errort-StatisticProb.LX
20.___
2030.
1002435.
8477990.0001C
2.
5258100.
8823502.
8625940.0154AR
10.
5671440.
2204572.5725___
0.0259R-squared
0.980054Meandependentvar
7.460096AdjustedR-squared
0.976428S.D.dependentvar
0.351331S.E.ofregression
0.053941Akaikeinfocriterion-
2.814442Sumsquaredresid
0.03xxSchwarzcriterion-
2.677501Loglikelihood
22.70109F-statistic
270.2458Durbin-Watsonstat
1.810100ProbF-statistic
0.000000InvertedARRoots.57DependentVariable:E1Method:LeastSquaresDate:06/07/05Time:21:00Sampleadjusted:1991xxIncludedobservations:14afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.E
20.
5017840.
2195612.
2853940.0413C
0.
0066390.
0150690.
4406000.6673R-squared
0.303258Meandependentvar
0.007495AdjustedR-squared
0.245197S.D.dependentvar
0.064877S.E.ofregression
0.056365Akaikeinfocriterion-
2.782368Sumsquaredresid
0.038124Schwarzcriterion-
2.691074Loglikelihood
21.47658F-statistic
5.223026Durbin-Watsonstat
1.517853ProbF-statistic
0.041274用1,2两种修正,两种效果都很好,都消除了自相关,相比较2更好所以,方程b=
0.502Y*=Ly-o.502*Ly(-1)X*=Lx2-
0.502*Lx2(-1)Y*=
1.2579+
0.___2X*+et以上就是通过分析和检验得到的回归方程所以,人均收入水平的高低在一定程度上影响房______当前的房______增长背后收入是不可忽略的因素中经网,国家______,模板内容仅供参考。