arXiv 1 Nov 2019 · Econometrics · publishedEconometric Theory (2023) · 6 citations (OpenAlex)
arXiv:1911.00166 · PDF · DOI · OpenAlex · Extracted main text
This paper studies large $N$ and large $T$ conditional quantile panel data models with interactive fixed effects. We propose a nuclear norm penalized estimator of the coefficients on the covariates and the low-rank matrix formed by the fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of the) fixed effects. It also allows the number of covariates to grow slowly with $N$ and $T$. We derive an error bound on the estimator that holds uniformly in quantile level. The order of the bound implies uniform consistency of the estimator and is nearly optimal for the low-rank component. Given the error bound, we also propose a consistent estimator of the number of fixed effects at any quantile level. To derive the error bound, we develop new theoretical arguments under primitive assumptions and new results on random matrices that may be of independent interest. We demonstrate the performance of the estimator via Monte Carlo simulations.
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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 | Agarwal, Negahban, and Wainwright (2012) Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions | 1.000 | 5 | 3 | 100% |
| 2 | Chernozhukov, Hansen, Liao, and Zhu (2019) Inference for heterogeneous effects using low-rank estimations | 1.000 | 5 | 3 | 100% |
| 3 | Ando and Bai (2020) Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity | 0.944 | 19 | 5 | 84% |
| 4 | Candès and Recht (2009) Exact matrix completion via convex optimization | 0.928 | 5 | 3 | 80% |
| 5 | Chen, Dolado, and Gonzalo (2020) Quantile factor models | 0.909 | 12 | 4 | 75% |
| 6 | Athey, Bayati, Doudchenko, Imbens, and Khosravi (2017) Matrix completion methods for causal panel data models | 0.843 | 3 | 3 | 100% |
| 7 | Moon and Weidner (2019) Nuclear norm regularized estimation of panel regression models | 0.811 | 4 | 2 | 100% |
| 8 | Belloni, Chen, Padilla, and Wang (2019) High dimensional latent panel quantile regression with an application to asset pricing | 0.769 | 11 | 4 | 45% |
| 9 | Candès, Li, Ma, and Wright (2011) Robust principal component analysis? | 0.737 | 4 | 3 | 50% |
| 10 | Harding and Lamarche (2014) Estimating and testing a quantile regression model with interactive effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Low-rank Panel Quantile Regression: Estimation and Inference | 0.644 | 3 | 2 |
| 2 | Detecting Latent Communities in Network Formation Models | 0.405 | 1 | 1 |
| 3 | Panel Data Models with Time-Varying Latent Group Structures | 0.405 | 1 | 1 |