TY  - RPRT
        AU  - Arulampalam, Wiji
AU  - Corradi, Valentina
AU  - Gutknecht, Daniel
        TI  - Intercept Estimation in Nonlinear Selection Models
        PY  - 2021/May/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 14364
        UR  - https://www.iza.org/index.php/publications/dp14364
        AB  - 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.
        KW  - irregular identification
KW  - selection bias
KW  - local polynomial
KW  - trimming
KW  - count data
        ER  -