Nonlinear Panel Data Methods for Multivariate Fractional Outcomes

Abstract

This paper expands the applied researcher’s toolkit for dealing with nonlinear panel data models with unobserved heterogeneity using multivariate fractional outcomes. It presents a wide range of methods that include maximum likelihood estimation for identifying the parameters of a conditional mean, a simple probit approach to identify average partial effects, and a Bayesian estimator from a latent dependent variable specification to account for corner outcomes. I then show how all these methods can be modified to handle continuous endogenous covariates. A range of simulation exercises showcase the comparative advantages of each method and how they might be used to approach different situations that arise in applied microeconomics.