Probit Regression is a technique used when the dependent variable is dichotomous (0 or 1). Unlike OLS, the methods used to estimate the parameters involve nonlinear approaches such as maximum likelihood. The probit specification is based on the Cumulative Normal Distribution. It assumes there exists a theoretical index Z(i) which is not observed or measured, but is linked to an explanatory variable X(i) which we have collected data on. The problem which Probit Regression solves is how to obtain estimates for the explanatory variable while at the same time obtain information about the underlying unmeasured scale of index X(i). Most computer programs perform this estimation process very quickly, even when the data sets are in the thousands.

One of the more useful aspects of Probit regression is that it outputs the probability of the event which will fall between 0 and 1 (0% to 100%). Therefore, given a set of independent variables, you could predict the probability of the event occurring using a Probit specification. This technique is designed around individual (disaggregated) cross-sectional data rather than time series data like that found in macroeconomic models. Other similar models are called Logistic Regression and Tobit Regression.