这应该不会呀，我此前用MLE估计模型时，用过这个选项，都是没有问题的。
你把运行的结果截个图给我看看。

arch x y , earch(1) egarch(1) het(z) ltolerance(0.0001)

Number of gaps in sample:  8  
(note: conditioning reset at each gap)


(setting optimization to BHHH)
Iteration 0:   log likelihood =  586.42721  
Iteration 1:   log likelihood =  597.54868  
Iteration 2:   log likelihood =  603.71421  
Iteration 3:   log likelihood =  607.30761  
Iteration 4:   log likelihood =   609.5603  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   610.3975  
Iteration 6:   log likelihood =  610.52171  
Iteration 7:   log likelihood =  610.81827  
Iteration 8:   log likelihood =  610.89797  
Iteration 9:   log likelihood =  610.90555  
Iteration 10:  log likelihood =  610.90643  
Iteration 11:  log likelihood =  610.90911  
Iteration 12:  log likelihood =  610.90912  *** 应该在此处就应该停止了

Iteration 13:  log likelihood =  610.90922  
Iteration 14:  log likelihood =  610.90924   
(switching optimization to BHHH)
Iteration 15:  log likelihood =  610.90924  
Iteration 16:  log likelihood =  610.90925  (backed up)   
Iteration 17:  log likelihood =  610.90925  (backed up)
Iteration 18:  log likelihood =  610.90925  (backed up)
Iteration 19:  log likelihood =  610.90925  (backed up)
(switching optimization to BFGS)
Iteration 20:  log likelihood =  610.90925  (backed up)
.................

(setting optimization to BHHH)
Iteration 0:   log likelihood =  439.03055  
Iteration 1:   log likelihood =  450.28815  
Iteration 2:   log likelihood =  465.40009  
Iteration 3:   log likelihood =  467.02226  
Iteration 4:   log likelihood =  467.16146  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  467.67814  
Iteration 6:   log likelihood =  467.82723  
Iteration 7:   log likelihood =   467.8609  
Iteration 8:   log likelihood =  467.89386  
Iteration 9:   log likelihood =  467.89716  
Iteration 10:  log likelihood =  467.89747  
Iteration 11:  log likelihood =  467.89962  
Iteration 12:  log likelihood =  467.89999  
Iteration 13:  log likelihood =  467.90005  
Iteration 14:  log likelihood =  467.90007  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =   467.9001  
Iteration 16:  log likelihood =  467.90011  (backed up)
Iteration 17:  log likelihood =  467.90011  (backed up)
Iteration 18:  log likelihood =  467.90011  (backed up)
Iteration 19:  log likelihood =  467.90011  (backed up)
(switching optimization to BFGS)
Iteration 20:  log likelihood =  467.90011  (backed up)
Iteration 21:  log likelihood =  467.90011  (backed up)
Iteration 22:  log likelihood =  467.90011  (backed up)
Iteration 23:  log likelihood =  467.90011  (backed up)
Iteration 24:  log likelihood =  467.90011  (backed up)
Iteration 25:  log likelihood =  467.90011  (backed up)
Iteration 26:  log likelihood =  467.90011  (backed up)
Iteration 27:  log likelihood =  467.90011  (backed up)
Iteration 28:  log likelihood =  467.90011  (backed up)
Iteration 29:  log likelihood =  467.90011  (backed up)
(switching optimization to BHHH)
Iteration 30:  log likelihood =  467.90011  (backed up)
Iteration 31:  log likelihood =  467.90011  (backed up)
Iteration 32:  log likelihood =  467.90011  (backed up)
Iteration 33:  log likelihood =  467.90011  (backed up)
Iteration 34:  log likelihood =  467.90011  (backed up)
(switching optimization to BFGS)
Iteration 35:  log likelihood =  467.90011  (backed up)
Iteration 36:  log likelihood =  467.90011  (backed up)
Iteration 37:  log likelihood =  467.90011  (backed up)
Iteration 38:  log likelihood =  467.90011  (backed up)

hehe,我看了help ，可能是由于“If the optimizer becomes stuck with repeated  "(backed up)" messages, the gradient probably still contains substantial values, but an uphill direction cannot be  found for the likelihood.  
”
所以我就设定gtolerance(999)选项，但是可能不是全局最优，或者设定最大的iterate次数，如iterate(50),但是发现与设定gtolerance(999)选项的结果差异很大。这时候应该怎么办？