ABSTRACT
The bivariate probit model Methods The ordered and multinomial models discussed in the previous two sections deal with dependent variables that can have different categorical outcomes. However, in both cases, there is a single underlying outcome variable. In contrast, the bivariate probit model provides a way of dealing with two separate binary dependent variables. Essentially it takes two independent binary probit models and estimates them together, allowing for a correlation between the error term of the two equations. The practical application discussed here uses the HALS data to estimate the probability of someone reporting ‘good’ or ‘excellent’ selfassessed health together with the probability of them being a current smoker. Allowing for correlation between the error terms of the two equations recognises that there may be unobservable characteristics of individuals that influence both whether they smoke and their self-assessed health.
