Intercept Estimation in Nonlinear Selection Models

Wiji Arulampalam, Valentina Corradi (v.corradi@surrey.ac.uk) and Daniel Gutknecht (daniel.gutknecht@economics.ox.ac.uk)
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Valentina Corradi: University of Surrey
Daniel Gutknecht: Goethe University Frankfurt

No 14364, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C14 C21 C24 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2021-05
New Economics Papers: this item is included in nep-ecm and nep-ore
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Published - published online in: Econometric Theory , 24 April 2023

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