Alexander Chudik, M. Hashem Pesaran, Mahrad Sharifvaghefi
arXiv 24 Dec 2023 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)
arXiv:2312.15494 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also investigated, and in-sample fit of the selected regression is shown to have the oracle property. The theoretical results support the use of unweighted observations at the selection stage of OCMT, whilst applying down-weighting of observations only at the forecasting stage. Monte Carlo and empirical applications show that OCMT without down-weighting at the selection stage yields smaller mean squared forecast errors compared to Lasso, Adaptive Lasso, and boosting.
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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 | Chudik, A., G. Kapetanios, and M. H. Pesaran (2018) A one covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models self | 1.000 | 9 | 6 | 100% |
| 2 | Lahiri, S. N (2021) Necessary and sufficient conditions for variable selection consistency of the lasso in high dimensions | 1.000 | 6 | 3 | 100% |
| 3 | Sharifvaghefi, M (2023) Variable selection in linear regressions with many highly correlated covariates self | 0.843 | 4 | 3 | 75% |
| 4 | Pesaran, M. H., A. Pick, and M. Pranovich (2013) Optimal forecasts in the presence of structural breaks self | 0.843 | 3 | 3 | 100% |
| 5 | Zhao, P. and B. Yu (2006) On model selection consistency of Lasso | 0.843 | 3 | 3 | 100% |
| 6 | Lütkepohl, H (1996) Handbook of Matrices | 0.811 | 4 | 2 | 100% |
| 7 | Bühlmann, P (2006) Boosting for high-dimensional linear models | 0.644 | 3 | 2 | 67% |
| 8 | Kapetanios, G. and F. Zikes (2018) Time-varying Lasso | 0.644 | 2 | 2 | 100% |
| 9 | Meinshausen, N. and P. Bühlmann (2006) High-dimensional graphs and variable selection with the lasso | 0.644 | 2 | 2 | 100% |
| 10 | Pesaran, M. H. and A. Timmermann (2007) Selection of estimation window in the presence of breaks self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 46 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | High-dimensional forecasting with known knowns and known unknowns | 0.737 | 3 | 2 |