自己先顶贴，求大神帮忙解答

看看这方面的文献，查查软件应用的书，这些东西自己慢慢扣比较好哦

广义矩估计其实限制条件并不多，stata命令为xtabond2
举例： xtabond2 n l.n l(0/1).(w k) yr1980-yr1984, gmm(l.n w k) iv(yr1980-yr1984, passthru) noleveleq small；
xtabond2 n L.n w L.w k yr*, gmm(L.(w n k)) iv(yr*) noleveleq robust small；
如果没有package需要下载安装：
findit xtabond2
找到对应安装包，install
如有疑问可以help xtabond2，会找到各种举例和详细介绍
其中，过度识别检验可以检验工具变量使用是否合理，一般需要检验残差是否二阶序列相关，因为一般差分后，干扰项会一阶序列相关。

GMM 不需要知道随机误差项的准确分布信息，允许随机误差项存在异方差和序列相关

做出来一个结果，可以帮我看看命令和回归结果的问题么，谢谢！

xtabond2 ISUt L1.ISUt fd fs trade gp,gmm(L3.ISUt L4.ISUt L2.fd L3.fd L2.fs L3.fs d.trade d.gp,e(d)) gmm(L2.d.ISUt L1.d.fs L1.d.fd trade gp,
> e(l)) twostep
Favoring speed over space. To switch, type or click on mata: mata set matafavor space, perm.
Warning: Number of instruments may be large relative to number of observations.
Warning: Two-step estimated covariance matrix of moments is singular.
  Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
  Difference-in-Sargan/Hansen statistics may be negative.

Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: province                        Number of obs      =       310
Time variable : year                            Number of groups   =        31
Number of instruments = 207                     Obs per group: min =        10
Wald chi2(5)  = 190497.79                                      avg =     10.00
Prob > chi2   =     0.000                                      max =        10
------------------------------------------------------------------------------
        ISUt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        ISUt |
         L1. |   .9243394   .0084803   109.00   0.000     .9077184    .9409605
             |
          fd |   .0035265   .0003857     9.14   0.000     .0027705    .0042826
          fs |  -.0029016   .0003025    -9.59   0.000    -.0034945   -.0023086
       trade |   .0151668   .0034547     4.39   0.000     .0083958    .0219378
          gp |   .0215032   .0015991    13.45   0.000      .018369    .0246373
       _cons |   .0186123   .0028894     6.44   0.000     .0129492    .0242754
------------------------------------------------------------------------------
Warning: Uncorrected two-step standard errors are unreliable.

Instruments for first differences equation
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    L(1/10).(L3.ISUt L4.ISUt L2.fd L3.fd L2.fs L3.fs D.trade D.gp)
Instruments for levels equation
  Standard
    _cons
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    DL(1/9).(L2D.ISUt LD.fs LD.fd trade gp)
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z =  -2.80  Pr > z =  0.005
Arellano-Bond test for AR(2) in first differences: z =   0.01  Pr > z =  0.994
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(201)  = 361.67  Prob > chi2 =  0.000
  (Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(201)  =  29.09  Prob > chi2 =  1.000
  (Robust, but weakened by many instruments.)

Difference-in-Hansen tests of exogeneity of instrument subsets:
  GMM instruments for levels
    Hansen test excluding group:     chi2(122)  =  29.91  Prob > chi2 =  1.000
    Difference (null H = exogenous): chi2(79)   =  -0.83  Prob > chi2 =  1.000
  gmm(L3.ISUt L4.ISUt L2.fd L3.fd L2.fs L3.fs D.trade D.gp, eq(diff) lag(1 .))
    Hansen test excluding group:     chi2(74)   =  29.19  Prob > chi2 =  1.000
    Difference (null H = exogenous): chi2(127)  =  -0.10  Prob > chi2 =  1.000
  gmm(L2D.ISUt LD.fs LD.fd trade gp, eq(level) lag(1 .))
    Hansen test excluding group:     chi2(122)  =  29.91  Prob > chi2 =  1.000
    Difference (null H = exogenous): chi2(79)   =  -0.83  Prob > chi2 =  1.000