A Doubly Robust GMM Estimator for Sequential Non-monotone Missingness
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866917543463092224 |
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| author | Yang, Shenshen |
| author_facet | Yang, Shenshen |
| contents | We study moment-based estimation with two sequentially collected variables subject to non-monotone missingness. The commonly used Missing at Random (MAR) assumption requiring all missingness mechanisms to depend on the same fully observed covariates often fails in such cases. We introduce a sequential MAR assumption that allows asymmetric missingness mechanisms across stages. Based on this assumption, we construct an Augmented Inverse-Probability-Weighted GMM (AIPW-GMM) estimator. The estimator features an asymmetric structure for the augmentation term, guarantees double robustness, and achieves the closed-form semiparametric efficiency bound. An application to two-period survey data from the Oregon Health Insurance Experiment supports the observable implications of the new assumption. The proposed approach reduces the standard errors by more than 50% for the estimated effects of the Oregon Health Plan among older adults, "driving" previously statistically insignificant estimates significant. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2201_01010 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Doubly Robust GMM Estimator for Sequential Non-monotone Missingness Yang, Shenshen Econometrics We study moment-based estimation with two sequentially collected variables subject to non-monotone missingness. The commonly used Missing at Random (MAR) assumption requiring all missingness mechanisms to depend on the same fully observed covariates often fails in such cases. We introduce a sequential MAR assumption that allows asymmetric missingness mechanisms across stages. Based on this assumption, we construct an Augmented Inverse-Probability-Weighted GMM (AIPW-GMM) estimator. The estimator features an asymmetric structure for the augmentation term, guarantees double robustness, and achieves the closed-form semiparametric efficiency bound. An application to two-period survey data from the Oregon Health Insurance Experiment supports the observable implications of the new assumption. The proposed approach reduces the standard errors by more than 50% for the estimated effects of the Oregon Health Plan among older adults, "driving" previously statistically insignificant estimates significant. |
| title | A Doubly Robust GMM Estimator for Sequential Non-monotone Missingness |
| topic | Econometrics |
| url | https://arxiv.org/abs/2201.01010 |