Victor Chernozhukov, Wolfgang K. Härdle, Chen Huang, Weining Wang
arXiv 13 Jun 2018 · Econometrics · publishedThe Annals of Statistics (2021) · 63 citations (OpenAlex)
arXiv:1806.05081 · PDF · DOI · OpenAlex · Extracted main text
We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak temporal dependence. A sequence of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable selection purpose, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the $Z$-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.
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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 | Belloni, Chernozhukov \ Kato (2015) Uniform post selection inference for least absolute deviation regression and other $Z$-estimation problems, Biometrika 102(1): 7… | 0.928 | 10 | 5 | 80% |
| 2 | Belloni, Chernozhukov \ Hansen (2011) Inference for high-dimensional sparse econometric models, arXiv preprint arXiv:1201.0220 | 0.874 | 5 | 2 | 100% |
| 3 | Chernozhukov, Chetverikov \ Kato (2013) Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors, The Annals of Statistics… | 0.843 | 4 | 4 | 75% |
| 4 | Belloni \ Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models, Bernoulli 19(2): 521–547 | 0.843 | 5 | 3 | 60% |
| 5 | Belloni, Chernozhukov \ Hansen (2014) Inference on treatment effects after selection among high-dimensional controls, The Review of Economic Studies 81(2): 608–650 | 0.737 | 3 | 2 | 100% |
| 6 | van de Geer, Bühlmann, Ritov \ Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models, The Annals of Statistics 42(3): 1166–1202 | 0.737 | 3 | 2 | 100% |
| 7 | Yuan \ Lin (2007) Model selection and estimation in the Gaussian graphical model, Biometrika 94(1): 19–35 | 0.737 | 3 | 2 | 100% |
| 8 | Zhang \ Zhang (2014) Confidence intervals for low dimensional parameters in high dimensional linear models, Journal of the Royal Statistical Society:… | 0.737 | 3 | 2 | 100% |
| 9 | Belloni, Chernozhukov \ Kato (2015) Supplement material for "Uniform post selection inference for least absolute deviation regression and other $Z$-estimation probl… | 0.693 | 6 | 2 | 50% |
| 10 | Zhang \ Wu (2017) Gaussian approximation for high dimensional time series, The Annals of Statistics 45(5): 1895–1919 | 0.669 | 10 | 3 | 30% |
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