@TechReport{iza:izadps:dp14364,
            	author={Arulampalam, Wiji and Corradi, Valentina and Gutknecht, Daniel},
            	title={Intercept Estimation in Nonlinear Selection Models},
            	year={2021},
	month={May},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={14364},
            	url={https://www.iza.org/publications/dp14364},
	abstract={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.},
	keywords={irregular identification;selection bias;local polynomial;trimming;count data},
            }