Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2015 050

Nonparametric Estimation in case of Endogenous Selection

Christoph Breunig
Enno Mammen
Anna Simoni

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In both cases, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Also pointwise asymptotic distribution of the estimators is established. In addition, we propose a nonparametric specification test to check the validity of our independence assumption. Finite sample properties are illustrated in a Monte Carlo simulation study and an empirical illustration.

Endogenous selection, instrumental variable, sieve minimum distance, regression estimation, convergence rate, asymptotic normality, hypothesis testing, inverse problem

JEL Classification:
C14, C26