arXiv 26 Apr 2025 · Econometrics
arXiv:2504.18772 · PDF · DOI · OpenAlex · Extracted main text
Panel data allows for the modeling of unobserved heterogeneity, significantly raising the number of nuisance parameters and making high dimensionality a practical issue. Meanwhile, temporal and cross-sectional dependence in panel data further complicates high-dimensional estimation and inference. This paper proposes a toolkit for high-dimensional panel models with large cross-sectional and time sample sizes. To reduce the dimensionality, I propose a weighted LASSO using two-way cluster-robust penalty weights. Although consistent, the convergence rate of LASSO is slow due to the cluster dependence, rendering inference challenging in general. Nevertheless, asymptotic normality can be established in a semiparametric moment-restriction model by leveraging a clustered-panel cross-fitting approach and, as a special case, in a partial linear model using the full sample. In a panel estimation of the government spending multiplier, I demonstrate how high dimensionality could be hidden and how the proposed toolkit enables flexible modeling and robust inference.
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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 | Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian an… (2016) Inference in high-dimensional panel models with an application to gun control | 0.956 | 8 | 4 | 88% |
| 2 | Chiang, Harold D. and Kato, Kengo and Ma, Yukun and Sasaki, Yuya (2022) Multiway cluster robust double/debiased machine learning | 0.928 | 5 | 4 | 80% |
| 3 | Nakamura, Emi and Steinsson, Jón (2014) Fiscal stimulus in a monetary union: Evidence from US regions | 0.874 | 11 | 2 | 100% |
| 4 | Chernozhukov, Victor and Karl Härdle, Wolfgang and Huang, Chen and W… (2021) Lasso-driven inference in time and space | 0.874 | 5 | 2 | 100% |
| 5 | Belloni, Alexandre and Chen, Daniel and Chernozhukov, Victor and Han… (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.843 | 15 | 4 | 60% |
| 6 | Gao, Jiti and Peng, Bin and Yan, Yayi (2024) Robust Inference for High-Dimensional Panel Data Models | 0.811 | 4 | 2 | 100% |
| 7 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.773 | 13 | 5 | 46% |
| 8 | Semenova, Vira and Goldman, Matt and Chernozhukov, Victor and Taddy,… (2023) Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence | 0.737 | 4 | 3 | 50% |
| clarke2025double | unmatched citation key clarke2025double | 0.737 | 3 | 2 | 100% |
| 10 | Chen, Kaicheng and Vogelsang, Timothy J (2024) Fixed-b asymptotics for panel models with two-way clustering self | 0.714 | 11 | 4 | 36% |
Showing the top 10 of 38 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 | Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence | 0.405 | 1 | 1 |