Anna Baiardi, Paul S. Clarke, Andrea A. Naghi, Annalivia Polselli
arXiv 20 Mar 2026 · Econometrics
arXiv:2603.20464 · PDF · OpenAlex · Extracted main text
Panel data methods are widely used in empirical analysis to address unobserved heterogeneity, but causal inference remains challenging when treatments are endogenous and confounding variables high-dimensional and potentially nonlinear. Standard instrumental variables (IV) estimators, such as two-stage least squares (2SLS), become unreliable when instrument validity requires flexibly conditioning on many covariates with potentially non-linear effects. This paper develops a Double Machine Learning estimator for static panel models with endogenous treatments (panel IV DML), and introduces weak-identification diagnostics for it. We revisit three influential migration studies that use shift-share instruments. In these settings, instrument validity depends on a rich covariate adjustment. In one application, panel IV DML strengthens the predictive power of the instrument and broadly confirms 2SLS results. In the other cases, flexible adjustment makes the instruments weak, leading to substantially more cautious causal inference than conventional 2SLS. Monte Carlo evidence supports these findings, showing that panel IV DML improves estimation accuracy under strong instruments and delivers more reliable inference under weak identification.
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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 | Andrews, Isaiah and Stock, James H and Sun, Liyang (2019) Weak Instruments in Instrumental Variables Regression | 1.000 | 5 | 3 | 100% |
| 2 | Clarke, Paul S and Polselli, Annalivia (2025) Double machine learning for static panel models with fixed effects self | 0.928 | 4 | 3 | 100% |
| 3 | Tabellini, Marco (2020) Gifts of the immigrants, woes of the natives: Lessons from the age of mass migration | 0.902 | 15 | 4 | 73% |
| 4 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.885 | 13 | 5 | 69% |
| 5 | Stock, James and Yogo, Motohiro (2005) Asymptotic distributions of instrumental variables statistics with many instruments | 0.881 | 19 | 7 | 68% |
| 6 | Lee, David S and McCrary, Justin and Moreira, Marcelo J and Porter,… (2022) Valid t-ratio Inference for IV | 0.874 | 12 | 6 | 67% |
| 7 | Moriconi, Simone and Peri, Giovanni and Turati, Riccardo (2022) Skill of the immigrants and vote of the natives: Immigration and nationalism in European elections 2007–2016 | 0.833 | 19 | 4 | 58% |
| 8 | Moriconi, Simone and Peri, Giovanni and Turati, Riccardo (2019) Immigration and voting for redistribution: Evidence from European elections | 0.807 | 19 | 4 | 53% |
| 9 | Bergstra, James and Bengio, Yoshua (2012) Random search for hyper-parameter optimization. | 0.737 | 4 | 3 | 50% |
| 10 | Davidson, Russell and MacKinnon, James G (2014) Confidence sets based on inverting Anderson–Rubin tests | 0.737 | 3 | 2 | 100% |
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