Laura Liu, Alexandre Poirier, Ji-Liang Shiu
arXiv 27 May 2021 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)
arXiv:2105.12891 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Manski (1987) Semiparametric analysis of random effects linear models from binary panel data | 0.928 | 5 | 3 | 80% |
| 2 | Imbens and Newey (2009) Identification and estimation of triangular simultaneous equations models without additivity | 0.874 | 8 | 2 | 100% |
| 3 | Altonji and Matzkin (2005) Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors | 0.874 | 7 | 2 | 100% |
| 4 | Bester and Hansen (2009) Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model | 0.874 | 5 | 2 | 100% |
| 5 | Davezies, D'Haultfoeuille, and Laage (2022) Identification and Estimation of Average Marginal Effects in Fixed Effect Logit Models | 0.811 | 4 | 2 | 100% |
| 6 | Ichimura and Lee (1991) Semiparametric least squares estimation of multiple index models: Single equation estimation | 0.794 | 6 | 4 | 50% |
| 7 | Botosaru and Muris (2024) Identification of time-varying counterfactual parameters in nonlinear panel models | 0.737 | 3 | 2 | 100% |
| 8 | Rasch (1960) Studies in mathematical psychology: I. Probabilistic models for some intelligence and attainment tests | 0.737 | 3 | 2 | 100% |
| 9 | Wooldridge (2010) Econometric Analysis of Cross Section and Panel Data | 0.737 | 3 | 2 | 100% |
| 10 | Abrevaya (1999) Leapfrog estimation of a fixed-effects model with unknown transformation of the dependent variable | 0.644 | 2 | 2 | 100% |
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