Actually, I suspect the problem is with the SARAR specification. That is really ill-behaved and
can lead to identification problems. Often the lag coefficient and error coefficients have opposite
signs (which doesn’t really make a lot of sense) and the estimates are very large in absolute value.
This can lead to problems with the GMM/IV estimators when the instruments are poor, e.g., when only
WX is used as an instrument. Unlike ML estimation, GMM/IV cannot guarantee that the estimate for the
spatial coefficient is in the proper parameter space, i.e., less than 1 in absolute value. This will make the
power expansion fail. There isn’t much one can do about this except trying higher order spatial lags of X
as instruments. But it generally points to a problem with SARAR. I personally avoid that specification unless
there is a very good (theoretical) reason.
L.