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A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and anything else that I can see (**except** for the value of the $J$-test) regardless of the weighting matrix. I do not think this is correct. The phenomenon persists regardless of wha...

...; > A copy of this question can be found on Cross Validated: > https://stats.stackexchange.com/questions/645362 > > I am estimating a system of seemingly unrelated regressions (SUR) in R. > Each of the equations has one unique regressor and one common regressor. I > am using `gmm::sysGmm` and am experimenting with different weighting > matrices. I get the same results (point estimates, standard errors and > anything else that I can see (**except** for the value of the $J$-test) > regardless of the weighting matrix. I do not think this is correct. > The phenomenon persis...

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645610 I am estimating a system of seemingly unrelated regressions (SUR) with `gmm::sysGmm` in R. Each of the equations has one unique regressor and one common regressor. The common regressor is a dummy variable indicating the last observation (n-1 zeros followed by 1). I impose a restriction that the coefficients on the common regressor are equal across equations. See a reproducible exa...