The EM principle implies the moments underlying the information matrix test for switching regressions are the expectation given the data of the moments one would test if one knew the subpopulation each observation originated from. Thus, we identify components related to conditional heteroskedasticity, conditional and unconditional skewness, and unconditional kurtosis of regression residuals within each regime. Simulations indicate analytical expressions for the asymptotic covariance matrix of those moments adjusted for sampling variability in parameter estimators provide reliable finite sample sizes and good power against various alternatives, especially combined with the parametric bootstrap. We apply the test to cross-country convergence regressions.