Variable selection of main effects is often a first step of model building followed by consideration of interactions and nonlinearities. We consider selection of second-order models under various hierarchy restrictions between main effects and second-order terms (squares and interactions) using the FSR approach of Wu et al. (2007, JASA) and Boos et al. (2009, Biometrics). The basic idea is to control the proportion of uninformative variables in the final model. Easy-to-use sas macros are available that implement these FSR variable selection approaches for linear regression, logistic regression, and Cox regression. This is a joint work with Hugh Crews and Len Stefanski.