arXiv 27 Aug 2023 · Econometrics
arXiv:2308.16192 · PDF · DOI · OpenAlex · Extracted main text
These lecture notes provide an overview of existing methodologies and recent developments for estimation and inference with high dimensional time series regression models. First, we present main limit theory results for high dimensional dependent data which is relevant to covariance matrix structures as well as to dependent time series sequences. Second, we present main aspects of the asymptotic theory related to time series regression models with many covariates. Third, we discuss various applications of statistical learning methodologies for time series analysis purposes.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Adamek, R., Smeekes, S., and Wilms, I (2023) Lasso inference for high-dimensional time series | 0.874 | 13 | 2 | 100% |
| 2 | Farrell, M. H., Liang, T., and Misra, S (2021) Deep neural networks for estimation and inference | 0.811 | 4 | 2 | 100% |
| 3 | Wong, K. C., Li, Z., and Tewari, A (2020) Lasso guarantees for $$-mixing heavy-tailed time series | 0.811 | 4 | 2 | 100% |
| 4 | Chen, Y., Cheng, C., and Fan, J (2021) Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices | 0.693 | 12 | 1 | 100% |
| 5 | Rinaldo, A., Wasserman, L., and G’Sell, M (2019) Bootstrapping and sample splitting for high-dimensional, assumption-lean inference | 0.693 | 9 | 1 | 100% |
| 6 | Shen, G., Jiao, Y., Lin, Y., Horowitz, J. L., and Huang, J (2021) Deep quantile regression: Mitigating the curse of dimensionality through composition | 0.693 | 9 | 1 | 100% |
| 7 | Hagemann, A (2012) A simple test for regression specification with non-nested alternatives | 0.693 | 7 | 1 | 100% |
| 8 | Reeve, H. W., Cannings, T. I., and Samworth, R. J (2021) Optimal subgroup selection | 0.693 | 6 | 1 | 100% |
| 9 | Dhrymes, P. J (2013) Mathematics for econometrics | 0.693 | 5 | 1 | 100% |
| 10 | Chen, X. and Liao, Z (2014) Sieve m inference on irregular parameters | 0.693 | 5 | 1 | 100% |
Showing the top 10 of 232 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Optimal Estimation Methodologies for Panel Data Regression Models | 0.644 | 2 | 2 |