Jiti Gao, Fei Liu, Bin Peng, Yayi Yan
arXiv 13 May 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2405.07420 · PDF · DOI · OpenAlex · Extracted main text
This paper provides the relevant literature with a complete toolkit for conducting robust estimation and inference about the parameters of interest involved in a high-dimensional panel data framework. Specifically, (1) we allow for non-Gaussian, serially and cross-sectionally correlated and heteroskedastic error processes, (2) we develop an estimation method for high-dimensional long-run covariance matrix using a thresholded estimator, (3) we also allow for the number of regressors to grow faster than the sample size. Methodologically and technically, we develop two Nagaev--types of concentration inequalities: one for a partial sum and the other for a quadratic form, subject to a set of easily verifiable conditions. Leveraging these two inequalities, we derive a non-asymptotic bound for the LASSO estimator, achieve asymptotic normality via the node-wise LASSO regression, and establish a sharp convergence rate for the thresholded heteroskedasticity and autocorrelation consistent (HAC) estimator. We demonstrate the practical relevance of these theoretical results by investigating a high-dimensional panel data model with interactive effects. Moreover, we conduct extensive numerical studies using simulated and real data examples.
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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 | Babii, Ball, Ghysels \ Striaukas (2023) `Machine learning panel data regressions with heavy-tailed dependent data: Theory and application', Journal of Econometrics 237(… | 1.000 | 9 | 4 | 100% |
| 2 | Belloni, Chen, Madrid Padilla \ Wang (2023) `High-dimensional latent panel quantile regression with an application to asset pricing', The Annals of Statistics 51(1), 96–121 | 0.956 | 8 | 5 | 88% |
| 3 | Vogt, Walsh \ Linton (2022) `CCE estimation of high-dimensional panel data models with interactive fixed effects', arXiv preprint arXiv:2206.12152 | 0.811 | 4 | 2 | 100% |
| 4 | Baek, Düker \ Pipiras (2023) `Local whittle estimation of high-dimensional long-run variance and precision matrices', The Annals of Statistics 51(6), 2386–2414 | 0.644 | 2 | 2 | 100% |
| 5 | Zou (2006) `The adaptive lasso and its oracle properties', Journal of the American Statistical Association 101(476), 1418–1429 | 0.644 | 2 | 2 | 100% |
| 6 | Chen \ Zimmermann (2022) `Open source cross-sectional asset pricing', Critical Finance Review 27(2), 207–264 | 0.585 | 3 | 1 | 100% |
| 7 | Gao, Peng \ Yan (2023) `Higher-order expansions and inference for panel data models', Journal of the American Statistical Association p. forthcoming | 0.511 | 2 | 2 | 50% |
| 8 | Kelly, Pruitt \ Su (2019) `Characteristics are covariances: A unified model of risk and return', Journal of Financial Economics 134(3), 501–524 | 0.511 | 2 | 1 | 100% |
| 9 | Pesaran (2021) `General diagnostic tests for cross section dependence in panels', Empirical Economics 60, 13–50 | 0.511 | 2 | 1 | 100% |
| 10 | Gupta \ Seo (2023) `Robust inference on infinite and growing dimensional time-series regression', Econometrica 91(4), 1333–1361 | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models$^*$ | 0.405 | 1 | 1 |