Robert Adamek, Stephan Smeekes, Ines Wilms
arXiv 21 Jul 2020 · Econometrics · publishedJournal of Econometrics (2022) · 6 citations (OpenAlex)
arXiv:2007.10952 · PDF · DOI · OpenAlex · Extracted main text
In this paper we develop valid inference for high-dimensional time series. We extend the desparsified lasso to a time series setting under Near-Epoch Dependence (NED) assumptions allowing for non-Gaussian, serially correlated and heteroskedastic processes, where the number of regressors can possibly grow faster than the time dimension. We first derive an error bound under weak sparsity, which, coupled with the NED assumption, means this inequality can also be applied to the (inherently misspecified) nodewise regressions performed in the desparsified lasso. This allows us to establish the uniform asymptotic normality of the desparsified lasso under general conditions, including for inference on parameters of increasing dimensions. Additionally, we show consistency of a long-run variance estimator, thus providing a complete set of tools for performing inference in high-dimensional linear time series models. Finally, we perform a simulation exercise to demonstrate the small sample properties of the desparsified lasso in common time series settings.
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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 | Medeiros, M. C. and E. F. Mendes (2016) $_1$-regularization of high-dimensional time-series models with non-gaussian and heteroskedastic errors | 1.000 | 9 | 4 | 100% |
| 2 | Masini, R. P., M. C. Medeiros, and E. F. Mendes (2022) Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations | 1.000 | 8 | 3 | 100% |
| 3 | Chernozhukov, V., W. K. Härdle, C. Huang, and W. Wang (2021) LASSO-driven inference in time and space | 1.000 | 5 | 3 | 100% |
| 4 | Babii, A., E. Ghysels, and J. Striaukas (2021) High-dimensional Granger causality tests with an application to VIX and news | 0.928 | 4 | 3 | 100% |
| 5 | van de Geer, S., P. Bühlmann, Y. Ritov, and R. Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.894 | 7 | 4 | 71% |
| 6 | Basu, S. and G. Michailidis (2015) Regularized estimation in sparse high-dimensional time series models | 0.843 | 3 | 3 | 100% |
| 7 | Krampe, J., J.-P. Kreiss, and E. Paparoditis (2021) Bootstrap based inference for sparse high-dimensional time series models | 0.843 | 3 | 3 | 100% |
| 8 | van de Geer, S. A (2016) Estimation and Testing under Sparsity | 0.737 | 3 | 3 | 67% |
| 9 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.737 | 3 | 2 | 100% |
| 10 | Kock, A. B. and L. Callot (2015) Oracle inequalities for high dimensional vector autoregressions | 0.737 | 3 | 2 | 100% |
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