Tests for classification as endogenous or predetermined of arbitrary subsets of regressors are formulated as significance tests in auxiliary IV regressions and their relationships with various more classic test procedures are examined. Simulation experiments are designed by solving the data generating process parameters from salient econometric features, namely: degree of simultaneity and multicollinearity of regressors, and individual and joint strength of external instrumental variables. Thus, for various test implementations, a wide class of relevant cases is scanned for flaws in performance regarding type I and II errors. Substantial size distortions occur, but these can be cured remarkably well through bootstrapping, except when instruments are weak. The power of the subset tests is such that they establish an essential addition to the well-known classic full-set DWH tests in a data based classification of individual explanatory variables.