%0 Report
        %A Arulampalam, Wiji
%A Corradi, Valentina
%A Gutknecht, Daniel
        %T Intercept Estimation in Nonlinear Selection Models
        %D 2021
        %8 2021 May
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 14364
        %U https://www.iza.org/index.php/publications/dp14364
        %X We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal distribution of instrument index is close to one. Such an estimator achieves a univariate nonparametric rate, which can range from a cubic to an 'almost' parametric rate. We then consider the case in which either the monotonic index restriction does not hold and/ or the set of observations with propensity score close to one is thin so that convergence occurs at most at a cubic rate. We explore the finite sample behaviour in a Monte Carlo study, and illustrate the use of our estimator using a model for count data with multiplicative unobserved heterogeneity.
        %K irregular identification
%K selection bias
%K local polynomial
%K trimming
%K count data