Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models

Fuente: arXiv
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Autori principali: Liu, Laura, Poirier, Alexandre, Shiu, Ji-Liang
Natura: Preprint
Pubblicazione: 2021
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author Liu, Laura
Poirier, Alexandre
Shiu, Ji-Liang
author_facet Liu, Laura
Poirier, Alexandre
Shiu, Ji-Liang
contents Average partial effects (APEs) are often not point identified in panel models with unrestricted unobserved individual heterogeneity, such as a binary response panel model with fixed effects and logistic errors as a special case. This lack of point identification occurs despite the identification of these models' common coefficients. We provide a unified framework to establish the point identification of various partial effects in a wide class of nonlinear semiparametric models under an index sufficiency assumption on the unobserved heterogeneity, even when the error distribution is unspecified and non-stationary. This assumption does not impose parametric restrictions on the unobserved heterogeneity and idiosyncratic errors. We also present partial identification results when the support condition fails. We then propose three-step semiparametric estimators for APEs, average structural functions, and average marginal effects, and show their consistency and asymptotic normality. Finally, we illustrate our approach in a study of determinants of married women's labor supply.
format Preprint
id arxiv_https___arxiv_org_abs_2105_12891
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models
Liu, Laura
Poirier, Alexandre
Shiu, Ji-Liang
Econometrics
Average partial effects (APEs) are often not point identified in panel models with unrestricted unobserved individual heterogeneity, such as a binary response panel model with fixed effects and logistic errors as a special case. This lack of point identification occurs despite the identification of these models' common coefficients. We provide a unified framework to establish the point identification of various partial effects in a wide class of nonlinear semiparametric models under an index sufficiency assumption on the unobserved heterogeneity, even when the error distribution is unspecified and non-stationary. This assumption does not impose parametric restrictions on the unobserved heterogeneity and idiosyncratic errors. We also present partial identification results when the support condition fails. We then propose three-step semiparametric estimators for APEs, average structural functions, and average marginal effects, and show their consistency and asymptotic normality. Finally, we illustrate our approach in a study of determinants of married women's labor supply.
title Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models
topic Econometrics
url https://arxiv.org/abs/2105.12891