<P>下面是我的协整试验结果,我研究的是人民币和其他货币之间的关系,这里有个问题,其它的货币系数感觉都是正确的,为什么韩元kr的系数会如此之大呢,标准化之后达到了510多,这可能是由什么原因造成的呢??谢谢</P>
<P>Date: 08/23/07   Time: 12:38     <BR>Sample: 1994:01 2007:01     <BR>Included observations: 154     <BR>Test assumption: Linear deterministic trend in the data     <BR>Series: RMB SIG TL TW UK USA ML KR JP HK EUR AUS      <BR>Warning: Critical values were derived for a maximum of 10 endogenous series     <BR>Lags interval: 1 to 2     <BR>     <BR> Likelihood 5 Percent 1 Percent Hypothesized <BR>Eigenvalue Ratio Critical Value Critical Value No. of CE(s) <BR>     <BR> 0.428931  421.1680 233.13?? 247.18??       None ** <BR> 0.357659  334.8903 233.13?? 247.18??    At most 1 ** <BR> 0.331253  266.7243 233.13 247.18    At most 2 ** <BR> 0.247230  204.7625 192.89 204.95    At most 3 * <BR> 0.232990  161.0271 156.00 168.36    At most 4 * <BR> 0.204676  120.1778 124.24 133.57    At most 5 <BR> 0.159967  84.91084  94.15 103.18    At most 6 <BR> 0.111708  58.06639  68.52  76.07    At most 7 <BR> 0.098061  39.82441  47.21  54.46    At most 8 <BR> 0.067493  23.93024  29.68  35.65    At most 9 <BR> 0.057113  13.16885  15.41  20.04    At most 10 <BR> 0.026350  4.112292   3.76   6.65    At most 11 * <BR>     <BR> *(**) denotes rejection of the hypothesis at 5%(1%) significance level     <BR> ?? denotes critical values derived assuming 10 endogenous series     <BR> L.R. test indicates 5 cointegrating equation(s) at 5% significance level     <BR>     <BR>            <BR>  RMB           SIG               TL             TW          UK            USA          ML             KR            JP             HK             EUR         AUS <BR> 0.678643 -0.679442 -2.447599 -9.873864  0.133405  0.860768  0.011394  346.3852  0.065677 -0.200997 -0.170397  0.091697 <BR> 1.624860  0.163611  14.82635 -6.695019 -0.049027 -0.275187 -0.685305 -68.26445  4.757041  3.264300  0.212483 -0.251135 <BR>-5.579193  0.110214 -2.906721  10.06385 -0.081077  4.060380  0.000275 -268.9400 -19.08846 -41.27109 -0.171900  0.292258 <BR> 2.176434 -0.225451 -0.284292  12.28935  0.047215 -5.417984  0.360699  40.47377  2.935063  40.10929  0.237097 -0.386614 <BR> 0.579784  0.026800  2.856811  9.667608 -0.002829  5.981291 -0.903977  38.20215 -14.21607 -44.89464 -0.112374  0.249832 <BR> 0.585674  1.126458 -1.431161 -4.684366 -0.180112  0.900815 -0.656401  114.7405 -7.224251  2.554477  0.178333 -0.133657 <BR>-0.630724  0.659172  1.551028 -1.664533  0.120204  0.968404 -0.493020  16.98803 -1.742970 -6.364713  8.49E-05 -0.328360 <BR> 0.684770  0.223725  4.565287 -2.443173 -0.140014 -3.581368 -0.666555 -11.47280  11.98885  28.76040  0.086397 -0.039955 <BR> 0.494850  0.417995  1.316558 -3.072364 -0.083630  2.286276 -0.238665 -13.07577  12.60831 -18.41934  0.028428  0.049427 <BR>-0.094158 -0.473291  0.404640  4.801122 -0.011593 -0.039260  0.400954 -79.11586  4.893320 -3.747201  0.038571 -0.030898 <BR> 0.080537  0.132757  1.819887  1.820036 -0.097735 -0.421411 -0.439760 -19.68385  0.109489  0.721074  0.057049  0.132480 <BR> 1.197924 -0.239754 -1.781076  0.712853  0.050622 -0.431963  0.229351  23.41113  8.645313  9.214141  0.026883  0.051946 <BR>            <BR>            <BR> Normalized Cointegrating Coefficients: 1 Cointegrating Equation(s)            <BR>            <BR>RMB SIG TL TW UK USA ML KR JP HK EUR AUS C<BR> 1.000000 -1.001176 -3.606606 -14.54942  0.196576  1.268365  0.016789  510.4083  0.096777 -0.296175 -0.251085  0.135118 -6.813219<BR>  (0.90110)  (4.56668)  (12.3758)  (0.17481)  (2.18222)  (0.23944)  (419.341)  (4.39470)  (11.3370)  (0.26736)  (0.19172) <BR>            <BR> Log likelihood  5880.658           <BR></P>