计量经济学分析报告.doc

中国银行界开始进行银行卡业务并没有太长的时间，但是银行卡业务的发展在整个银行体系中有着十分重要的作用，鉴于此，我们决定分析一下是哪些因素在影响银行卡业务的交易额。为了找出影响银行卡交易额的因素，我们选择了工商银行，农业银行，中国银行，建设银行，招商银行六家银行的数据（中国金融年鉴19962000）。设模型为Y=1+2X1+3X2+4X3+5X4+6X5+7X6其中， Y 银行卡业务交易额（万元）X1发卡机构（个）X2发卡量 （张）X3特约商户（个）X4取现网点（个）X5ATM机 （台）X6POS机 （台）表1obsYX1X2X3X4X5X614027090029019221730694532450335653079221315000034646138735426335513102021248328890000495471000053044118201934165954205050005021087590046600217002687161655679600483488149627145697240762000104652563531268047975615000030830088814814902762054994657083042800038788390377159140522475238149927150000446809445661841128472454717810144632003052065830049774231104045302481127120059109526112204205129562191264001315465899901461382412137691800030743885083895952898869175631814475500034014986461819564273933364961015251100003301277251168182139652859268941622670500303310979934549121179456930063175395006532581861589827207089141181960014337889135.9616325526891980770000300548377479752929519828356834206731341032522125836857584225035725078221267319001631774821871871139303206386632228561800296492471124931924779571739503234595600.377669239331997027831115127522423787000136426048639918343614006首先用OLS进行模型估计表2Dependent Variable: YMethod: Least SquaresDate: 12/12/03 Time: 23:02Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-3680754.6063061.-0.6070790.5518X1-15105.4243742.79-0.3453240.7341X20.5001830.6317890.7916930.4394X3588.1427357.52131.6450560.1183X4-416.5884538.2219-0.7740090.4496X51143.4925413.3740.2112350.8352X612.18085764.49840.0159330.9875R-squared0.760328 Mean dependent var24739109Adjusted R-squared0.675738 S.D. dependent var24354861S.E. of regression13868623 Akaike info criterion35.96665Sum squared resid3.27E+15 Schwarz criterion36.31025Log likelihood-424.5998 F-statistic8.988400Durbin-Watson stat1.811537 Prob(F-statistic)0.000164由上表可以看出，模型拟合还好，但是T检验都不显著，并且有些系数还出现了与经济意义相背离的现象。说明所选模型存在问题，必须进行修正。检验是否有多重共线性。表3X1X2X3X4X5X6X110.368973970.758389210.717380000.579503820.51270456X20.3689739710.662649980.536521530.924538420.81155867X30.758389210.6626499810.848732050.834731340.89361429X40.717380000.536521530.8487320510.703326820.83280296X50.579503820.924538420.834731340.7033268210.89090771X60.512704560.811558670.893614290.832802960.890907711可以看出除X1与X2的相关性较小，其余解释变量之间存在相关关系较大。选出X1，X2，X3 ，X4，X5，X6中对Y影响较显著的X3进行辅助回归。表4Dependent Variable: X3Method: Least SquaresDate: 12/13/03 Time: 01:24Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-2125.9793965.646-0.5360990.5985X181.8070221.443953.8149240.0013X2-0.0006800.000384-1.7703030.0936X4-0.3867300.342924-1.1277420.2742X53.2263703.4869010.9252830.3671X61.5314470.3517524.3537740.0004R-squared0.935210 Mean dependent var47634.75Adjusted R-squared0.917213 S.D. dependent var31777.10S.E. of regression9143.135 Akaike info criterion21.29171Sum squared resid1.50E+09 Schwarz criterion21.58623Log likelihood-249.5005 F-statistic51.96433Durbin-Watson stat2.338614 Prob(F-statistic)0.000000因为F=(O.935210/(6-1)/(1-0.93521O)/(24-6)=51.96413，而查表F0.05（5,18）=2.77，51.96413显著大于2.77，所以可判断模型存在多重共线性。首先用Y对X3进行单独回归。表5Dependent Variable: YMethod: Least SquaresDate: 12/13/03 Time: 01:20Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-4253338.5649935.-0.7528120.4595X3608.640799.308616.1287810.0000R-squared0.630637 Mean dependent var24739109Adjusted R-squared0.613847 S.D. dependent var24354861S.E. of regression15134396 Akaike info criterion35.98249Sum squared resid5.04E+15 Schwarz criterion36.08066Log likelihood-429.7899 F-statistic37.56196Durbin-Watson stat1.971714 Prob(F-statistic)0.000004可以看出X3单独对Y 的影响显著。用Y 对X2 X3进行回归表6Dependent Variable: YMethod: Least SquaresDate: 12/13/03 Time: 01:21Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-4298503.4882566.-0.8803780.3886X20.6721450.2311022.9084330.0084X3387.7936114.59053.3841690.0028R-squared0.736697 Mean dependent var24739109Adjusted R-squared0.711621 S.D. dependent var24354861S.E. of regression13078790 Akaike info criterion35.72735Sum squared resid3.59E+15 Schwarz criterion35.87461Log likelihood-425.7282 F-statistic29.37806Durbin-Watson stat1.909223 Prob(F-statistic)0.000001X2，X3联合对Y 的影响比X2，X3单独对Y 的影响大。加入X1进行OLS，得表7Dependent Variable: YMethod: Least SquaresDate: 12/13/03 Time: 01:22Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-3159302.5160254.-0.6122380.5473X1-20483.4227193.55-0.7532450.4601X20.6221040.2427862.5623600.0186X3483.2526171.66172.8151440.0107R-squared0.743961 Mean dependent var24739109Adjusted R-squared0.705555 S.D. dependent var24354861S.E. of regression13215626 Akaike info criterion35.78271Sum squared resid3.49E+15 Schwarz criterion35.97905Log likelihood-425.3925 F-statistic19.37102Durbin-Watson stat1.951553 Prob(F-statistic)0.000004拟合并没有显著变优，同时，X1的符号与经济意义相反，所以去除X1。用同样的做法可以去除X4, X5, X6。因此，模型为Y= 4298503+0.672145X2+387.7936x3 （式1） (4882566) (0.231102) (114.5905) t=(-0.880378) (2.908433) (3.384169)R2=0.736697 df=21进行异方差检验。ARCH检验表8ARCH Test:F-statistic0.162997 Probability0.919791Obs*R-squared0.587160 Probability0.899366Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/13/03 Time: 01:25Sample(adjusted): 4 24Included observations: 21 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C1.80E+148.59E+132.1002250.0509RESID2(-1)-0.0083670.246741-0.0339110.9733RESID2(-2)0.0471040.2479600.1899640.8516RESID2(-3)-0.1721790.251874-0.6835920.5034R-squared0.027960 Mean dependent var1.64E+14Adjusted R-squared-0.143576 S.D. dependent var2.66E+14S.E. of regression2.84E+14 Akaike info criterion69.57035Sum squared resid1.38E+30 Schwarz criterion69.76931Log likelihood-726.4887 F-statistic0.162997Durbin-Watson stat1.909327 Prob(F-statistic)0.919791WHITE检验表9White Heteroskedasticity Test:F-statistic1.334828 Probability0.293323Obs*R-squared5.264877 Probability0.261183Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/13/03 Time: 01:26Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C5.62E+131.12E+140.5007280.6223X221313860158366241.3458590.1942X22-0.3692220.274141-1.3468340.1939X3-7.49E+097.07E+09-1.0597130.3026X3290907.9071582.571.2699720.2194R-squared0.219370 Mean dependent var1.50E+14Adjusted R-squared0.055027 S.D. dependent var2.51E+14S.E. of regression2.44E+14 Akaike info criterion69.27764Sum squared resid1.13E+30 Schwarz criterion69.52307Log likelihood-826.3317 F-statistic1.334828Durbin-Watson stat2.251078 Prob(F-statistic)0.293323由以上两种检验联合判断式1可能不会存在异方差，但是由于所用数据为截面数据，所以，也有必要进行修正。进行修正：首先用GENR生成数据LY=logy，LX2=logx2，LX3=logx3，数据如下：表10 LY LX2 LX31 17.51114 16.77155 11.148412 16.39193 15.34458 10.901603 17.17901 15.36520 10.878884 16.83618 16.20206 10.749365 13.42926 12.76229 9.1723276 7.600902 13.05034 5.8664687 17.84354 17.21966 11.308248 17.23087 15.99469 11.178729 17.11689 15.90669 11.0323210 16.48712 16.84363 10.8152511 12.51061 13.90650 9.40951912 8.764053 14.25156 6.89770513 18.15825 17.59709 11.4030514 15.37471 16.52266 11.3139415 17.03878 16.36281 11.1299416 16.93658 17.25265 10.7252717 13.19840 14.99668 9.67394918 9.883285 15.03306 3.58240719 18.20712 17.81989 11.4879120 18.02487 16.91226 11.3592821 17.10137 16.69180 11.1826322 17.16758 17.71236 10.8060623 15.34061 15.75049 9.90198624 16.98465 15.67587 8.763897将以上数据用OLS得出：表11Dependent Variable: LYMethod: Least SquaresDate: 12/13/03 Time: 01:17Sample: 1 24Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-7.4912002.933308-2.5538400.0185LX20.8317970.2327053.5744680.0018LX30.9738900.1628715.9795010.0000R-squared0.862135 Mean dependent var15.51324Adjusted R-squared0.849005 S.D. dependent var3.0370