Robert Adamek, Stephan Smeekes, Ines Wilms
arXiv 2 Feb 2023 · Econometrics
arXiv:2302.01233 · PDF · DOI · OpenAlex · Extracted main text
We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest.
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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 | Krampe, J., J.-P. Kreiss, and E. Paparoditis (2021) Bootstrap based inference for sparse high-dimensional time series models | 0.956 | 8 | 4 | 88% |
| 2 | Adamek, R., S. Smeekes, and I. Wilms (2023) Lasso inference for high-dimensional time series self | 0.874 | 10 | 2 | 100% |
| 3 | Kock, A. B. and L. Callot (2015) Oracle inequalities for high dimensional vector autoregressions | 0.874 | 7 | 2 | 100% |
| 4 | Kock, A. B., R. S. Pedersen, and J. R.-V. Srensen (2024) Data-driven tuning parameter selection for high-dimensional vector autoregressions | 0.843 | 3 | 3 | 100% |
| 5 | Chernozhukov, V., D. Chetverikov, and Y. Koike (2023) Nearly optimal central limit theorem and bootstrap approximations in high dimensions | 0.754 | 7 | 3 | 43% |
| 6 | Chernozhukov, V., D. Chetverikov, and K. Kato (2013) Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors | 0.737 | 3 | 2 | 100% |
| 7 | Hecq, A., L. Margaritella, and S. Smeekes (2023) Inference in non-stationary high-dimensional VARs | 0.644 | 2 | 2 | 100% |
| 8 | Nicholson, W. B., I. Wilms, J. Bien, and D. S. Matteson (2020) High dimensional forecasting via interpretable vector autoregression | 0.644 | 2 | 2 | 100% |
| 9 | Paparoditis, E (1996) Bootstrapping autoregressive and moving average parameter estimates of infinite order vector autoregressive processes | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, V., D. Chetverikov, and K. Kato (2017) Central limit theorems and bootstrap in high dimensions | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 37 scored citations.