Empirical researchers are frequently confronted with issues regarding which explanatory variables to include in their models. This article describes the application of a well-known model-selection algorithm to Stata: general-to-specific (GETS) modeling. This process provides a prescriptive and defendable way of selecting a few relevant variables from a large list of potentially important variables when fitting a regression model. Several empirical issues in GETS modeling are then discussed, specifically, how such an algorithm can be applied to estimations based upon cross-sectional, time-series, and panel data. A command is presented, written in Stata and Mata, that implements this algorithm for various data types in a flexible way. This command is based on Stata’s regress or xtreg command, so it is suitable for researchers in the broad range of fields where regression analysis is used. Finally, the genspec command is illustrated using data from applied studies of GETS modeling with Monte Carlo simulation. It is shown to perform as empirically predicted and to have good size and power (or gauge and potency) properties under simulation.