Jonas Krampe, Luca Margaritella
arXiv 14 Dec 2021 · Statistics — Methodology · 3 citations (OpenAlex)
arXiv:2112.07149 · PDF · DOI · OpenAlex · Extracted main text
We reconcile the two worlds of dense and sparse modeling by exploiting the positive aspects of both. We employ a factor model and assume {the dynamic of the factors is non-pervasive while} the idiosyncratic term follows a sparse vector autoregressive model (VAR) {which allows} for cross-sectional and time dependence. The estimation is articulated in two steps: first, the factors and their loadings are estimated via principal component analysis and second, the sparse VAR is estimated by regularized regression on the estimated idiosyncratic components. We prove the consistency of the proposed estimation approach as the time and cross-sectional dimension diverge. In the second step, the estimation error of the first step needs to be accounted for. Here, we do not follow the naive approach of simply plugging in the standard rates derived for the factor estimation. Instead, we derive a more refined expression of the error. This enables us to derive tighter rates. We discuss the implications of our model for forecasting, factor augmented regression, bootstrap of factor models, and time series dependence networks via semi-parametric estimation of the inverse of the spectral density matrix.
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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 | Bai, J. & Ng, S (2002) Determining the number of factors in approximate factor models | 1.000 | 13 | 5 | 100% |
| 2 | Bai, J (2003) Inferential Theory for Factor Models of Large Dimensions | 1.000 | 11 | 3 | 100% |
| 3 | Stock, J. H. & Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 1.000 | 6 | 4 | 100% |
| 4 | Forni, M., Hallin, M., Lippi, M., & Reichlin, L (2000) The generalized dynamic-factor model: Identification and estimation | 0.928 | 4 | 3 | 100% |
| 5 | Krampe, J. & Paparoditis, E (2021) Sparsity concepts and estimation procedures for high dimensional vector autoregressive models self | 0.894 | 7 | 4 | 71% |
| 6 | Wu, W. B. & Zaffaroni, P (2018) Asymptotic theory for spectral density estimates of general multivariate time series | 0.894 | 7 | 3 | 71% |
| 7 | Basu, S., Li, X., & Michailidis, G (2019) Low rank and structured modeling of high-dimensional vector autoregressions | 0.874 | 6 | 2 | 100% |
| 8 | Lin, J. & Michailidis, G (2020) System identification of high-dimensional linear dynamical systems with serially correlated output noise components | 0.874 | 6 | 2 | 100% |
| 9 | Miao, K., Phillips, P. C., & Su, L (2022) High-dimensional vars with common factors | 0.874 | 5 | 2 | 100% |
| 10 | Forni, M., Hallin, M., Lippi, M., & Zaffaroni, P (2017) Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis | 0.822 | 6 | 2 | 83% |
Showing the top 10 of 68 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When? | 1.000 | 21 | 6 |
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