arXiv 4 Nov 2022 · Econometrics
arXiv:2211.02215 · PDF · DOI · OpenAlex · Extracted main text
Assessing the statistical significance of parameter estimates is an important step in high-dimensional vector autoregression modeling. Using the least-squares boosting method, we compute the p-value for each selected parameter at every boosting step in a linear model. The p-values are asymptotically valid and also adapt to the iterative nature of the boosting procedure. Our simulation experiment shows that the p-values can keep false positive rate under control in high-dimensional vector autoregressions. In an application with more than 100 macroeconomic time series, we further show that the p-values can not only select a sparser model with good prediction performance but also help control model stability. A companion R package boostvar is developed.
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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 | Lutz, R. W. and Bühlmann, P (2006) Boosting for high-multivariate responses in high-dimensional linear regression | 1.000 | 5 | 3 | 100% |
| 2 | McCracken, M. W. and Ng, S (2016) Fred-md: A monthly database for macroeconomic research | 0.811 | 4 | 2 | 100% |
| 3 | Freund, R. M., Grigas, P. and Mazumder, R (2017) A new perspective on boosting in linear regression via subgradient optimization and relatives | 0.763 | 9 | 3 | 44% |
| 4 | Hamilton, J (1994) Time Series Analysis | 0.737 | 5 | 3 | 40% |
| 5 | Tibshirani, R. J., Taylor, J., Lockhart, R. and Tibshirani, R (2016) Exact post-selection inference for sequential regression procedures | 0.737 | 3 | 2 | 100% |
| 6 | Chen, L. and Huang, J. Z (2012) Sparse reduced-rank regression for simultaneous dimension reduction and variable selection | 0.644 | 2 | 2 | 100% |
| 7 | Lütkepohl, H (2005) New Introduction to Multiple Time Series Analysis | 0.644 | 2 | 2 | 100% |
| 8 | Uematsu, Y., Fan, Y., Chen, K., Lv, J. and Lin, W (2019) Sofar: Large-scale association network learning | 0.644 | 2 | 2 | 100% |
| 9 | Horn, R. A. and Johnson, C. R (1985) Matrix Analysis | 0.511 | 2 | 2 | 50% |
| 10 | Lockhart, R., Taylor, J., Tibshirani, R. J. and Tibshirani, R (2014) A significance test for the lasso | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 29 scored citations.