Simplified Marginal Effects in Discrete Choice Models

Abstract

We show that after a simple normalization of explanatory variables so that they equal zero at some desired reference point, marginal effects for continuous variables in probit and logit models simplify dramatically, becoming a function of only the estimated constant term. We present similar simplifications for computation of the asymptotic variance of marginal effects, as well as for the effects of dummy variables on predicted probabilities. We provide a simple table, which in combination with raw probit or logit estimates, is all one needs to compute the desired effects.logit, probit, discrete choice, binary choice, marginal effect, data normalization