On Semiparametric Estimation of the Intercept of the Sample Selection Model

A Kernel Approach

Bibliographic Data

ID21512980
AuthorsZhewen Pan (0009-0004-7426-6976, Center for Quantitative Economic Research and School of Economics Zhejiang University of Finance and Economics Hangzhou China, corresponding author)
Year2026
Publication date2026-02-03
Open AccessYes
TypeARTICLE
VenueOxford Bulletin of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0305-9049 • E-ISSN: 1468-0084
PublisherWiley (PUBLISHER • GB)
DOI10.1111/obes.70050
OpenAlexW7127390130
LanguageEN
References cited35

This paper presents a novel perspective on the identification at infinity as identification at the boundary, for the intercept of the sample selection model, via a transformation of the selection index. This perspective suggests generalisations of estimation at infinity to kernel regression estimation at the boundary and further to local linear estimation at the boundary. The proposed kernel‐type estimators with an estimated transformation are proven to be nonparametric‐rate consistent and asymptotically normal under mild regularity conditions. A fully data‐driven method of selecting the optimal bandwidths for the estimators is developed. The Monte Carlo simulation shows the desirable finite sample properties of the proposed estimators and bandwidth selection procedures

Estimator · Infinity · Kernel method · Kernel regression · Monte Carlo method · Semiparametric regression · Advanced Statistical Methods and Models · Control Systems and Identification · Statistical Methods and Inference

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