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The paper discusses identification conditions, representations and relationsof generalized least squares estimators of regression parameters inmultivariate linear regression models such as seemingly unrelated and fixedeffect panel models. Results are presented on identification for unrestricteddispersion structure and general heteroskedasticity and cross-equationdependence, considering explicit and implicit restrictions, singularity of thedispersion and multicollinearity in the design matrix.

Seemingly unrelated and fixed-effect panel regressions: collinearity and singular dispersion