Many of us, at some point, have received a comment from a member of the audience, a reviewer, or an advisor who thinks the technique used is bad/biased/evil and who knows of some new fancy method that solves the problem. In those cases, you often want to know two things: 1) How big is the problem? and 2) Does that new fancy method actually work? In this talk, I will demonstrate how to answer these questions using the simulate command in Stata. I will illustrate using the following two examples: First, say we have a dependent variable that is collected not as a continuous variable but as a series of ranges, e.g., wage measured in categories ($0–5/hour, $6–10/hour, etc.). How bad is it to assign each category its middle value and treat it as a continuous variable? How much better is intreg at dealing with this problem? Second, various approaches are proposed if we have missing data. The default in Stata (and most other packages) is to ignore all observations with missing data. Official Stata also contains the impute command, and there is the user-written ice command by Patrick Royston. This raises the question of which method is the best.
