影响上证指数因素分析.doc

计量经济学课程论文影响上证指数因素分析小组成员:（信息管理与信息系统05级） 李健40511107吴双405111122007年12月一、理论分析“上证指数”全称“上海证券交易所综合股价指数”，是国内外普遍采用的反映上海股市总体走势的统计指标。上证指数是一个派许公式计算的以报告期发行股数为权数的加权综合股价指数。 报告期指数=（报告期采样股的市价总值/基日采样股的市价总值）100上证指数与股价息息相关，所以我们先来看看影响股价的因素有哪些：影响股票价格变动的因素很多，但基本上可分为 以下三类：市场内部因素，基本面因素，政策因素。 （1）市场内部因素它主要是指市场的供给和需求， 即资金面和筹码面的相对比例，如一定阶段的股市扩 容节奏将成为该因素重要部分。（2）基本面因素 包括宏观经济因素和公司内部因素，宏观经济因素主 要是能影响市场中股票价格的因素，包括经济增长， 经济景气循环，利率，财政收支，货币供应量，物价， 国际收支，汇率等，公司内部因素主要指公司的财务状况。 （3）政策因素是指足以影响股票价格变动的国内外 重大活动以及政府的政策，措施，法令等重大事件， 政府的社会经济发展计划，经济政策的变化，新颁布 法令和管理条例等均会影响到股价的变动。我们主要讨论基本面因素：货币供应量、汇率及市场内部因素：投资者开户数。我们可用得出汇率，货币供应量，投资者是影响股价的重要因素，即影响上证指数的重要因素。二、实证分析我们从中国统计网上找到了时间为2002年1月到2006年12月的相关数据，样本量为60。数据如下：上证指数（Y）日期2002200320042005200611396.611499.821590.731191.821258.0521524.71511.931675.0713061299.0331603.911510.981741.621181.241298.341667.751521.441595.591159.151440.2251515.731576.261555.911060.741641.361732.961489.021399.161080.941672.2171651.591476.741386.21083.031612.7381666.621421.981342.061162.81658.6491581.621367.161396.71155.611752.42101507.51348.31320.541092.821837.99111434.181397.231340.771099.262099.29121357.651497.041266.51161.062675.47汇率（人民币对美元X1）日期2002200320042005200618.27728.27688.27698.27658.066828.27728.27738.27718.27658.049338.27728.27728.27718.27658.03548.27728.27718.27698.27658.015658.27728.27698.27718.27658.015268.27728.27708.27678.27658.006778.27728.27738.27678.23697.99188.27728.27708.27688.10197.973398.27728.27718.27678.09227.9368108.27728.27678.27658.08897.9032118.27728.27698.27658.0847.8652128.27728.27708.27658.07597.8238投资者开户数（A股+B股X2）日期2002200320042005200616673.636895.937039.877221.057347.5426689.296904.017076.357228.027361.336730.966914.047104.027242.477382.8646759.946930.997123.477252.87414.6356785.356943.567133.747260.5747602966804.176959.547146.687273.937526.0376823.586971.077156.587281.357566.7186840.676979.947164.47295.37759101996850.986988.957118.777312.767615.68106860.426996.357187.577319.677650.18116871.327008.417198.167328.457694.11126884.087025.417211.437336.077854广义货币供给量（X3）日期200220032004200520061159669190488.3225076257752.8303538.62160935.6190108.4227050.7259356.1304516.33164064.6194487.3231654.6264588.9310490.74164571196130233628266993313701.85166061199505234842269229.4313701.86169601.2204907.4238427.5275785.5322756.47170851.1206193238127276952324010.88173250.9210592239729281288327885.79176982213567.1243756.9287438.3331865.410177294214469243740.3287591.633274711179736.321635224713629200033800012185007221222.8253207.7298755.5345577.9狭义货币供给量（X4）日期2002200320042005200616057772405.78379097079107389.8258702.969756.683556.492815104357.1359474.871438.885815.694743.2106737.1460461713218560494594106389.1561284.9727788678095801.310921966314475923.288627.198601.3112342.4763487.8761538798297663112653864868.8770338912599378114845.796679779163.990439.1100964116814.110671008026790782.51017521183601167992.880815923871040001220001270881.884118.695970.8107278.6126028.1以上证指数为被解释变量Y，以人民币对美元的汇率为解释变量X1，以投资者开户数为解释变量X2，以广义货币供应量为解释变量X3，以狭义货币供应量为解释变量X4建立多元线性回归模型：Y=a0+a1*X1+a2*X2+a3*X3+a4*X4+U (U为随机扰动项)利用EVIEWS软件估计模型如下：Included observations: 60VariableCoefficientStd. Errort-StatisticProb.X40.0520280.0126814.1029660.0001X3-0.0222620.004386-5.0761060.0000X21.62E-051.85E-050.8791880.3831X1-2719.847344.7086-7.8902790.0000C24583.232965.9788.2884060.0000R-squared0.558559Mean dependent var1464.162Adjusted R-squared0.526454S.D. dependent var265.8698S.E. of regression182.9574Akaike info criterion13.33604Sum squared resid1841039.Schwarz criterion13.51057Log likelihood-395.0812F-statistic17.39797Durbin-Watson stat0.561589Prob(F-statistic)0.000000初步方程为：Y = 24583.22673 - 2719.84735*X1 + 1.622415122e-005*X2 - 0.02226179382*X3 + 0.05202810665*X4模型检验：(一) 经济意义检验：x3的符号不符合经济理论的假设，因此经济意义检验不能通过。(二) 统计检验：（1） 拟合优度检验：可决系数=0.558559，修正可决系数=0.526454，拟合效果不是很好，说明还有其他解释变量对被解释变量产生影响，介于所学知识有限，在此不引入其他变量。（2） T检验：给定显著性水平0.05，针对H0：a1=a2=a3=a4=0,查t分布表得自由度为n-k=60-5=55的临界值等于2.011，x2不能通过t检验，说明在其他解释变量不变的情况下，投资者开户数对被解释变量没有显著的影响。（3） F检验：给定显著性水平0.05，查表得自由度为k-1=4和n-k=60-5=55的临界值为2.54，F=17.39797大于临界值，应拒绝原假设，说明回归方程显著。三个解释变量联合起来对被解释变量的影响是显著的。（三）计量检验： A）多重共线性检验：t检验跟F检验综合判断法：F检验显著，整个回归方程显著，但是x2不能通过t检验，对因变量的影响不显著，表明模型很有可能存在多重共线性。简单相关系数判断法： 得到的相关系数矩阵如下：X4X3X2X1X41.0000000.9940350.251542-0.811950X30.9940351.0000000.268685-0.832330X20.2515420.2686851.000000-0.297395X1-0.811950-0.832330-0.2973951.000000可以看出：x3跟x4存在严重的正相关，x1和x3存在严重的负相关。多重共线性的修正：（逐步回归法）首先对自变量x1，x2，x3，x4进行对数变换，重新拟合模型如下：Y = 54941.41711 - 19358.60483*LX1 + 14.31618099*LX2 - 5161.65511*LX3 + 4486.983161*LX4第一步：运用OLS方法分别求Y对各解释变量lx1，lx2，lx3，lx4进行一元回归。四个方程的回归结果如下：Y C L X1VariableCoefficientStd. Errort-StatisticProb.C15344.594218.4803.6374680.0006LX1-6597.1762004.929-3.2904790.0017R-squared0.157311Mean dependent var1464.162Adjusted R-squared0.142781S.D. dependent var265.8698S.E. of regression246.1585Akaike info criterion13.88259Sum squared resid3514451.Schwarz criterion13.95240Log likelihood-414.4778F-statistic10.82725Durbin-Watson stat0.210992Prob(F-statistic)0.001705Y C LX2VariableCoefficientStd. Errort-StatisticProb.C1206.686252.08234.7868730.0000LX228.2832527.433211.0309860.3068R-squared0.017997Mean dependent var1464.162Adjusted R-squared0.001065S.D. dependent var265.8698S.E. of regression265.7281Akaike info criterion14.03559Sum squared resid4095463.Schwarz criterion14.10540Log likelihood-419.0677F-statistic1.062931Durbin-Watson stat0.224652Prob(F-statistic)0.306828Y C LX3VariableCoefficientStd. Errort-StatisticProb.C1738.1761889.6010.9198640.3615LX3-22.14564152.6906-0.1450360.8852R-squared0.000363Mean dependent var1464.162Adjusted R-squared-0.016873S.D. dependent var265.8698S.E. of regression268.1034Akaike info criterion14.05339Sum squared resid4169006.Schwarz criterion14.12320Log likelihood-419.6016F-statistic0.021035Durbin-Watson stat0.196000Prob(F-statistic)0.885185Y C LX4VariableCoefficientStd. Errort-StatisticProb.C1525.4881905.6390.8005130.4267LX4-5.396617167.6652-0.0321870.9744R-squared0.000018Mean dependent var1464.162Adjusted R-squared-0.017223S.D. dependent var265.8698S.E. of regression268.1496Akaike info criterion14.05373Sum squared resid4170443.Schwarz criterion14.12354Log likelihood-419.6120F-statistic0.001036Durbin-Watson stat0.195728Prob(F-statistic)0.974434结合经济意义跟统计检验选出最好的一元线性回归方程，选取Lx1进入回归模型的第一个解释变量，形成一元回归模型。第二步：逐步回归：将剩余解释变量分别加入模型，结果如下：Y C LX1 LX2VariableCoefficientStd. Errort-StatisticProb.C15230.644573.2223.3303960.0015LX1-6550.9752133.442-3.0706130.0033LX21.83877227.041820.0679970.9460R-squared0.157379Mean dependent var1464.162Adjusted R-squared0.127813S.D. dependent var265.8698S.E. of regression248.2983Akaike info criterion13.91585Sum squared resid3514166.Schwarz criterion14.02056Log likelihood-414.4754F-statistic5.323030Durbin-Watson stat0.210246Prob(F-statistic)0.007595Y C LX1 LX3VariableCoefficientStd. Errort-StatisticProb.C49556.257488.7116.6174600.0000LX1-17227.292647.321-6.5074420.0000LX3-957.3820185.1111-5.1719310.0000R-squared0.426460Mean dependent var1464.162Adjusted R-squared0.406336S.D. dependent var265.8698S.E. of regression204.8516Akaike info criterion13.53116Sum squared resid2391957.Schwarz criterion13.63587Log likelihood-402.9347F-statistic21.19142Durbin-Watson stat0.306046Prob(F-statistic)0.000000Y C LX1 LX4VariableCoefficientStd. Errort-StatisticProb.C44428.337520.5385.9076000.0000LX1-15524.442665.010-5.8252850.0000LX4-906.4538204.5882-4.4306260.0000R-squared0.373183Mean dependent var1464.162Adjusted R-squared0.351189S.D. dependent var265.8698S.E. of regression214.1549Akaike info criterion13.61998Sum squared resid2614153.Schwarz criterion13.72470Log likelihood-405.5995F-statistic16.96778Durbin-Watson stat0.281521Prob(F-statistic)0.000002通过观察比较，选取LX3作为第二个变量引入模型第三步：在保留LX1，LX3的基础上，继续进行逐步回归，得到结果如下：Y C LX1 LX3 LX2VariableCoefficientStd. Errort-StatisticProb.C49197.167621.9806.4546430.0000LX1-17071.032707.882-6.3042010.0000LX3-960.5297186.7986-5.1420600.0000LX27.60991722.513480.3380160.7366R-squared0.427628Mean dependent var1464.162Adjusted R-squared0.396965S.D. dependent var265.8698S.E. of regression206.4620Akaike info criterion13.56245Sum squared resid2387087.Schwarz criterion13.70207Log likelihood-402.8735F-statistic13.94616Durbin-Watson stat0.304644Prob(F-statistic)0.000001Y C LX1 LX3 LX4VariableCoefficientStd. Errort-StatisticProb.C55513.716987.8387.9443320.0000LX1-19611.242489.234-7.8784250.0000LX3-5084.2131145.655-4.4378210.0000LX44410.5461211.1933.6414900.0006R-squared0.536269Mean dependent var1464.162Adjusted R-squared0.511426S.D. dependent var265.8698S.E. of regression185.8378Akaike info criterion13.35197Sum squared resid1933999.Schwarz criterion13.49159Log likelihood-396.5590F-statistic21.58655Durbin-Watson stat0.505557Prob(F-statistic)0.000000通过结果分析，在lx1，lx3的基础上引入lx4后R2有了改进，而且各参数的t检验显著，F检验显著，相应的p值都7.815, 则拒绝原假设，说明模型的随机误差项存在异方差。异方差的修正：加权最小二乘法我们分别选用权数w1=1/Lx1,w2=1/LX3 w31/LX3,w4=1/LX12,w5=LX42,w6=LX32,w7=1/sqr(LX1), w8=1/sqr(Lx2),w9=1/sqr(LX3),经估计检验发现选用权数w4=1/LX12的效果最好。下面仅给出w4的结果：Included observations: 60Weighting series: W4VariableCoefficientStd. Errort-StatisticProb.C56470.667006.0698.0602500.0000LX1-19983.632481.528-8.0529520.0000LX3-5244.2211146.036-4.5759660.0000LX44569.5001213.0763.7668700.0004Weighted StatisticsR-squared0.580721Mean dependent var1465.789Adjusted R-squared0.558259S.D. dependent var281.8614S.E. of regression187.3352Akaike info criterion13.36802Sum squared resid1965290.Schwarz criterion13.50764Log likelihood-397.0405F-sta