We propose a shape constrained nonparametric IV estimator which imposes a set of shape constraints on a nonparametric IV approach. We apply the Landweber–Fridman regularization to the Shape Constrained Kernel–weighted Least Squares (SCKLS) estimator developed by Yagi et al. (2018). Furthermore, we also consider more complicated shape constraints proposed by microeconomic theory by applying iterative S–shape algorithm proposed by Yagi et al. (2018). We aim to improve the finite sample performance and the economic interpretability of estimated results by imposing correctly specified shape constraints while avoiding the bias from endogeneity issues.
Posted in Ongoing work.