Speaker: Yuan Liao (UMCP)

Title: Nonparametric Instrumental Variable Regression


In nonparametric regressions, when the regressor is correlated with the error term, both the estimation and identification of the nonparametric function are ill posed problems. In the econometric literature, people have been using the instrumental variables to solve the problem. But the problem is still very difficult because the identification involves inverting a "Fredholm integration of the first kind", whose inverse either does not exist or is unbounded. I will start by motivating this problem with an application of the effect of education on wage, then explain the concepts of instrumental variables and Fredholm integral equation of the first kind. My proposed Bayesian method does not require the nonparametric function to be identified, so we can never consistently estimate it. Instead, a new consistency concept based on ``partial identification" will be introduced.