我的计量基础很差，最近做毕业论文时遇到了很大的问题，想请各位高手予以指点：

我是要根据一组面板数据做一个probit或者logit回归，首先对这些指标进行过corr，相关系数大多都在0.1以内，少数几个在0.5以下，做VIF分析，也都在2一下，是不是可以不考虑共线性了呢？

但是回归的结果感觉不大好，大多数系数的z值都很小，而且其它相关指标我的理解也很模糊，这两天虽然一直在网上查阅相关资料，但总还是看得云里雾里的。先贴一个试验结果发上来：

Fitting comparison model:

Iteration 0:   log likelihood = -198.93324
Iteration 1:   log likelihood =   -89.5573
Iteration 2:   log likelihood = -71.344916
Iteration 3:   log likelihood = -67.369216
Iteration 4:   log likelihood = -66.924545
Iteration 5:   log likelihood = -66.917215
Iteration 6:   log likelihood = -66.917213

Fitting full model:

rho =  0.0     log likelihood = -92.512473
rho =  0.1     log likelihood = -55.467123
rho =  0.2     log likelihood = -41.736322
rho =  0.3     log likelihood = -33.637198
rho =  0.4     log likelihood = -27.967127
rho =  0.5     log likelihood = -23.714651
rho =  0.6     log likelihood = -20.197696
rho =  0.7     log likelihood = -17.289147
rho =  0.8     log likelihood = -14.803667

Iteration 0:   log likelihood = -16.824675 
Iteration 1:   log likelihood = -13.217479 
Iteration 2:   log likelihood = -12.605991 
Iteration 3:   log likelihood = -12.587627 
Iteration 4:   log likelihood = -12.587611 
Iteration 5:   log likelihood = -12.587611 

Random-effects probit regression                Number of obs      =       287
Group variable (i): country                     Number of groups   =        13

Random effects u_i ~ Gaussian                   Obs per group: min =        15
                                                               avg =      22.1
                                                               max =        24

                                                Wald chi2(11)      =      7.91
Log likelihood  = -12.587611                    Prob > chi2        =    0.7214

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         rer |  -3.475526   3.158579    -1.10   0.271    -9.666228    2.715176
       res_g |    .825807   1.651825     0.50   0.617    -2.411711    4.063325
      m2_res |   .9094768   2.506475     0.36   0.717    -4.003125    5.822078
    m2_res_g |   1.246309   1.961459     0.64   0.525     -2.59808    5.090698
       gdp_g |   1.384571   1.648769     0.84   0.401    -1.846958    4.616099
      dc_gdp |   3.991885   2.858283     1.40   0.163    -1.610247    9.594017
      ca_gdp |  -7.376085   3.668452    -2.01   0.044    -14.56612   -.1860506
       rl_rd |  -1.043828   2.217364    -0.47   0.638    -5.389782    3.302125
       exp_g |  -.5325338   1.768384    -0.30   0.763    -3.998502    2.933435
        im_g |   .6575589   1.705087     0.39   0.700    -2.684351    3.999469
         inf |   .2479812    1.57941     0.16   0.875    -2.847606    3.343568
-------------+----------------------------------------------------------------
    /lnsig2u |   2.113526   .4843254                      1.164265    3.062786
-------------+----------------------------------------------------------------
     sigma_u |   2.877043   .6967125                      1.789852    4.624615
         rho |   .8922109   .0465779                      .7621069    .9553313
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =   108.66 Prob >= chibar2 = 0.000

想请各位给我指点一下：     1、 Log likelihood  = -12.587611         Wald chi2(11)      Prob > chi2        =    0.7214

                                                     /lnsig2u      sigma_u          rho |  

                                                        Likelihood-ratio test of rho=0: chibar2(01) =   108.66 Prob >= chibar2 = 0.000

                                                      这些检验都应该怎么去看？

                                          2、像这种表现不好的模型可以从哪些方面改进呢？或者说其问题可能出在哪里了呢？

                 快答辩了，可是这个实证困扰我很久了，真头大~~

                  期盼您的解答，不胜感激！！！