Ilias Chronopoulos, Katerina Chrysikou, George Kapetanios
arXiv 14 Jul 2022 · Econometrics
arXiv:2207.07055 · PDF · DOI · OpenAlex · Extracted main text
In this paper we develop inference for high dimensional linear models, with serially correlated errors. We examine Lasso under the assumption of strong mixing in the covariates and error process, allowing for fatter tails in their distribution. While the Lasso estimator performs poorly under such circumstances, we estimate via GLS Lasso the parameters of interest and extend the asymptotic properties of the Lasso under more general conditions. Our theoretical results indicate that the non-asymptotic bounds for stationary dependent processes are sharper, while the rate of Lasso under general conditions appears slower as $T,p\to \infty$. Further we employ the debiased Lasso to perform inference uniformly on the parameters of interest. Monte Carlo results support the proposed estimator, as it has significant efficiency gains over traditional methods.
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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 | Davidson (1994) Stochastic limit theory: An introduction for econometricians | 1.000 | 9 | 5 | 100% |
| 2 | Chudik et al (2018) A one covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models | 1.000 | 8 | 3 | 100% |
| 3 | Dendramis et al (2021) Estimation of time-varying covariance matrices for large datasets | 1.000 | 5 | 3 | 100% |
| 4 | Van de Geer et al (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.961 | 9 | 4 | 89% |
| 5 | Bühlmann and Van De Geer (2011) Statistics for high-dimensional data: methods, theory and applications | 0.874 | 6 | 2 | 100% |
| 6 | Kock (2016) Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models | 0.843 | 3 | 3 | 100% |
| 7 | Leeb and Pötscher (2005) Model selection and inference: Facts and fiction | 0.843 | 3 | 3 | 100% |
| 8 | Raskutti et al (2010) Restricted eigenvalue properties for correlated Gaussian designs | 0.843 | 3 | 3 | 100% |
| 9 | Wong et al (2020) Lasso guarantees for $$-mixing heavy-tailed time series | 0.644 | 2 | 2 | 100% |
| 10 | Javanmard and Montanari (2014) Confidence intervals and hypothesis testing for high-dimensional regression | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.