Jianqing Fan, Ricardo Masini, Marcelo C. Medeiros
arXiv 22 Feb 2021 · Econometrics · publishedThe Annals of Statistics (2023) · 45 citations (OpenAlex)
arXiv:2102.11341 · PDF · DOI · OpenAlex · Extracted main text
Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting method that combines the merits of these two models in a supervised learning methodology that allows for efficiently exploring all the information in high-dimensional datasets. The method is based on a flexible model for high-dimensional panel data, called factor-augmented regression model with observable and/or latent common factors, as well as idiosyncratic components. This model not only includes both principal component regression and sparse regression as specific models but also significantly weakens the cross-sectional dependence and facilitates model selection and interpretability. The method consists of several steps and a novel test for (partial) covariance structure in high dimensions to infer the remaining cross-section dependence at each step. We develop the theory for the model and demonstrate the validity of the multiplier bootstrap for testing a high-dimensional (partial) covariance structure. The theory is supported by a simulation study and applications.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | barticle[author] Fan, J.J., Liao, Y.Y. Mincheva, M.M (2013) ) | 0.928 | 4 | 4 | 100% |
| 2 | bbook[author] Rio, EmmanuelE (2017) ) | 0.874 | 5 | 2 | 100% |
| 3 | barticle[author] Bai, J.J (2003) ) | 0.843 | 3 | 3 | 100% |
| 4 | barticle[author] Bai, J.J. Ng, S.S (2002) ) | 0.843 | 3 | 3 | 100% |
| 5 | barticle[author] Fan, J.J., Ke, Y.Y. Wang, K.K (2020) ) | 0.811 | 4 | 2 | 100% |
| 6 | barticle[author] Andrews, Donald W. K.D. W. K (1991) ) | 0.737 | 3 | 2 | 100% |
| 7 | barticle[author] Pesaran, M. H.M. H (2006) ) | 0.737 | 3 | 2 | 100% |
| 8 | bbook[author] Fan, J.J., Li, R.R., Zhang, C. H.C. H. Zou, H.H (2020) ) | 0.644 | 2 | 2 | 100% |
| 9 | barticle[author] Bernanke, B. S.B. S., Boivin, J.J. Eliasz, P.P (2005) ) | 0.644 | 2 | 2 | 100% |
| 10 | barticle[author] Brownlees, C.C., Gudmundsson, G. S.G. S. Lugosi, G.G (2020) ) | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.