Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models

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Tác giả: Laura Liu, Alexandre Poirier, Ji-Liang Shiu

Ngôn ngữ: eng

Ký hiệu phân loại: 001.434 Experimental method

Thông tin xuất bản: 2021

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Bộ sưu tập: Metadata

ID: 167057

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.
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