Grigory Franguridi, Arie Kapteyn
arXiv 24 Aug 2026 · Econometrics
arXiv:2608.23508 · PDF · Extracted main text
In panels with sample selection (that may occur due to attrition, nonresponse, etc.), the assumption of selection on observables (missing at random, MAR) is commonly imposed despite often being implausible. However, this assumption becomes testable when a refreshment sample is available. We develop a statistical test of MAR based on a distance between two estimated distributions: one obtained using the standard inverse probability weighting (IPW) that is valid under MAR and the other obtained using an alternative weighting that is valid under a weaker assumption of additive nonignorability of Hirano et al. (2001). This test implicitly compares the distribution of the IPW-weighted sample in the attrition period with the distribution of the refreshment sample, which coincide if the MAR assumption holds. We establish that, when the input distributions are parametric, our test statistic converges to the generalized chi-squared distribution under the null of MAR. This limit distribution can be estimated using the recursive formulas derived by Franguridi et al. (2026). We illustrate the performance of our test in Monte Carlo simulations. Finally, we apply our test to an empirical example using a subsample of the Understanding America Study (UAS) dataset.
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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 | Franguridi, Grigory and Hahn, Jinyong and Hoonhout, Pierre and Kapte… (2026) Raking for estimation and inference in panel models with nonignorable attrition and refreshment self | 1.000 | 11 | 5 | 100% |
| 2 | Hirano, Keisuke and Imbens, Guido W and Ridder, Geert and Rubin, Don… (2001) Combining Panel Data Sets with Attrition and Refreshment Samples | 1.000 | 5 | 4 | 100% |
| 3 | Rüschendorf, Ludger (1995) Convergence of the iterative proportional fitting procedure | 0.511 | 2 | 1 | 100% |
| 4 | Bhattacharya, Debopam (2008) Inference in panel data models under attrition caused by unobservables | 0.405 | 1 | 1 | 100% |
| 5 | Chen, Heng and Felt, Marie-Hélène and Huynh, Kim P (2017) Retail payment innovations and cash usage: accounting for attrition by using refreshment samples | 0.405 | 1 | 1 | 100% |
| 6 | Deng, Yiting and Hillygus, D Sunshine and Reiter, Jerome P and Si, Y… (2013) Handling attrition in longitudinal studies: The case for refreshment samples | 0.405 | 1 | 1 | 100% |
| 7 | Franguridi, Grigory and Kosenkova, Lidia (2026) Closed-form estimation and inference for panels with attrition and refreshment samples self | 0.405 | 1 | 1 | 100% |
| 8 | Franguridi, Grigory and Liu, Laura (2026) Inference in partially identified moment models via regularized optimal transport self | 0.405 | 1 | 1 | 100% |
| 9 | Franguridi, G and Hahn, J and Kapteyn, Arie and Ridder, G (2026) Robust estimation and inference with given marginals self | 0.405 | 1 | 1 | 100% |
| 10 | Hausman, Jerry A and Wise, David A (1979) Attrition bias in experimental and panel data: the Gary income maintenance experiment | 0.405 | 1 | 1 | 100% |
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