Abstract

Endogeneity is the most severe failure that a discrete choice model can face in its objective of making causal inference or forecasting. It occurs when the explanatory variables are not independent of the error term and results in inconsistent estimators of the model parameters. This chapter summarizes the causes, impact, and methods to detect and to address endogeneity in discrete choice models. The emphasis is put on providing the theoretical fundamentals and the intuition behind the problems that may arise, as well as practical details as on how to apply the methods to address them, stressing the main considerations that must be taken in this endeavor. The main concepts are formally stated, but the reader is referred to specific articles for further details that may be required for advanced applications or a deeper understanding. The chapter begins providing details on the definition, impact, causes and examples of endogeneity, followed by a revision of possible approaches to detect this problem. Then, a deep review of the fundamentals, intuition and practicalities of the various methods that can be used to address endogeneity is presented, followed by a section providing insights and methods related to the obtention of instrumental variables, which are key in this effort. The chapter continues then revising the problem of forecasting with discrete choice models that have been corrected for endogeneity, finalizing with a section that summarizes the main conclusions and takeaways of the chapter.