Paper Title
Instrumental Variable Estimation for Functional Concurrent Regression Models

Abstract
In this work, we adopt a novel approach to studying labor supply elasticities using functional regression models. We use data from the Current Population Survey, and use age and years of working experience as instrumental variables for wage. There is some recent work in the literature adapting instrumental variables to functional regression models, however these have to date focused on scalar-on-function regression models. We instead use a functional concurrent model, which is one special case of a function-on-function regression model. Furthermore, our estimation method is appropriate for sparse functional data, and show through simulations that it greatly eliminates the bias introduced by a naive model (i.e., one that does not acknowledge endogeneity) and produces accurate coefficient estimates for moderate sample sizes. Keywords - Functional Concurrent model, Instrumental Variables, Sparse Functional Data.