Yet Another Post on LPM and Probit?
Binary choice models. Statistical models used to analyze the determinants of a binary outcome. Employed or not employed. Enrolled in school or not enrolled. Defaulted on a loan or not. WTO member or not. Democratic or not. And on and on. The analysis of binary outcomes is frequent and important in economics, political science, sociology, epidemiology, and others. So, we should strive to get it right. Getting it "right" has meant a lengthy debate - that has seemingly gone on ad nau seam - over the choice between a linear probability model (LPM) and a probit or logit model. For those unaware, LPM is a fancy name for "I am going to use OLS even though the dependent variable is binary, but I want to feel special!" Probit and logit, on the other hand, are estimated via maximum likelihood (the original ML). Now that we have covered the basics, here is the twist. This is most definitely NOT another blog post on the relative merits of LPM and probit/logit