Misspecification in trivariate probit models with recursive structure and sample selection

Music City Center 

This paper examines the consequences of model misspecification when data are generated from a trivariate probit model that accounts for recursive dependencies and sample selection. We investigate the estimation bias that arises under different model specifications, either ignoring recursive structures, sample selection, or both. In addition to providing theoretical results, we conduct Monte Carlo simulations to quantify the bias magnitude, not only in the parameters associated with the explanatory variables in the three equations, but also in the correlation parameters of the corresponding error terms. We highlight the risk of misinterpreting these correlation parameters, which could lead to invalid conclusions about the potential presence of selection bias. Our findings emphasize the importance of careful model specification in applications involving multiple binary outcomes, where selection bias and recursive structures can play an important role in shaping the results.

trivariate probit models

sample selection

recursive models

Simulated Maximum Likelihood estimation 

Business and Economic Statistics Section