Guohua Feng, Jiti Gao, Fei Liu, Bin Peng
arXiv 12 Apr 2024 · Econometrics · 7 citations (OpenAlex)
arXiv:2404.08365 · PDF · DOI · OpenAlex · Extracted main text
Hierarchical panel data models have recently garnered significant attention. This study contributes to the relevant literature by introducing a novel three-dimensional (3D) hierarchical panel data model, which integrates panel regression with three sets of latent factor structures: one set of global factors and two sets of local factors. Instead of aggregating latent factors from various nodes, as seen in the literature of distributed principal component analysis (PCA), we propose an estimation approach capable of recovering the parameters of interest and disentangling latent factors at different levels and across different dimensions. We establish an asymptotic theory and provide a bootstrap procedure to obtain inference for the parameters of interest while accommodating various types of cross-sectional dependence and time series autocorrelation. Finally, we demonstrate the applicability of our framework by examining productivity convergence in manufacturing industries worldwide.
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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 | Jin, Lu \ Su (2024) Three-dimensional factor models with global and local factors | 0.928 | 4 | 3 | 100% |
| 2 | Jin, Lu \ Su (2024) Three-dimensional heterogeneous panel data models with multi-level interactive fixed effects | 0.928 | 4 | 3 | 100% |
| 3 | Rodrik (2013) `Unconditional convergence in manufacturing', Quarterly Journal of Economics 128(1), 165–204 | 0.874 | 5 | 2 | 100% |
| 4 | Bai (2009) `Panel data models with interactive fixed effects', Econometrica 77(4), 1229–1279 | 0.737 | 4 | 3 | 50% |
| 5 | Shao (2010) `The dependent wild bootstrap', Journal of the American Statistical Association 105(489), 218–235 | 0.737 | 3 | 3 | 67% |
| 6 | Chen, Yang \ Zhang (2022) `Factor models for high-dimensional tensor time series', Journal of the American Statistical Association 117(537), 94–116 | 0.737 | 3 | 2 | 100% |
| 7 | Gao, Peng \ Yan (2023) `Higher-order expansions and inference for panel data models', Journal of the American Statistical Association 0(0), 1–12 | 0.644 | 2 | 2 | 100% |
| 8 | Kapetanios, Serlenga \ Shin (2021) `Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure', Jo… | 0.644 | 2 | 2 | 100% |
| 9 | Matyas et al (2017) The Econometrics of Multi-dimensional Panels: Theory and Applications, Springer, Chamg | 0.644 | 2 | 2 | 100% |
| 10 | Zhang, Pan, Yao \ Zhou (2023) `Factor modeling for clustering high-dimensional time series', Journal of the American Statistical Association 0(0), 1–12 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.