arXiv 2 Dec 2024 · Econometrics
arXiv:2412.01208 · PDF · DOI · OpenAlex · Extracted main text
Existing identification and estimation methods for semiparametric sample selection models rely heavily on exclusion restrictions. However, it is difficult in practice to find a credible excluded variable that has a correlation with selection but no correlation with the outcome. In this paper, we establish a new identification result for a semiparametric sample selection model without the exclusion restriction. The key identifying assumptions are nonlinearity on the selection equation and linearity on the outcome equation. The difference in the functional form plays the role of an excluded variable and provides identification power. According to the identification result, we propose to estimate the model by a partially linear regression with a nonparametrically generated regressor. To accommodate modern machine learning methods in generating the regressor, we construct an orthogonalized moment by adding the first-step influence function and develop a locally robust estimator by solving the cross-fitted orthogonalized moment condition. We prove root-n-consistency and asymptotic normality of the proposed estimator under mild regularity conditions. A Monte Carlo simulation shows the satisfactory performance of the estimator in finite samples, and an application to wage regression illustrates its usefulness in the absence of exclusion restrictions.
appendix boundary found by appendix_titled_section at “Appendix” · 47% of the source is main text. Read the extracted text to check this.
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 | Honoré, B. E. and L. Hu (2020) Selection without exclusion | 1.000 | 19 | 3 | 100% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 3 | 100% |
| 3 | Robinson, P (1988) Root-n-consistent semiparametric regression | 0.950 | 7 | 4 | 86% |
| 4 | Lee, D. S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.874 | 11 | 2 | 100% |
| chamberlain1986asymptotic | unmatched citation key chamberlain1986asymptotic | 0.874 | 6 | 2 | 100% |
| 6 | Hahn, J. and G. Ridder (2013) Asymptotic variance of semiparametric estimators with generated regressors | 0.843 | 5 | 4 | 60% |
| 7 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally robust semiparametric estimation | 0.811 | 4 | 2 | 100% |
| 8 | Ahn, H. and J. L. Powell (1993) Semiparametric estimation of censored selection models with a nonparametric selection mechanism | 0.644 | 2 | 2 | 100% |
| 9 | Pan, Z., Z. Wang, J. Zhang, and Y. Zhou (2024) Marginal treatment effects in the absence of instrumental variables self | 0.644 | 2 | 2 | 100% |
| 10 | Heckman, J (1979) Sample specification bias as a selection error | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 56 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder | 0.405 | 1 | 1 |
| 2 | Identification and Estimation of Semiparametric Multilayered Sample Selection Models | 0.405 | 1 | 1 |