Ji Hyung Lee, Zhentao Shi, Zhan Gao
arXiv 7 Oct 2018 · Econometrics · publishedJournal of Econometrics (2021) · 1 citations (OpenAlex)
arXiv:1810.03140 · PDF · DOI · OpenAlex · Extracted main text
Explanatory variables in a predictive regression typically exhibit low signal strength and various degrees of persistence. Variable selection in such a context is of great importance. In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework. In the presence of stationary, local unit root, and cointegrated predictors, we show that the adaptive LASSO cannot asymptotically eliminate all cointegrating variables with zero regression coefficients. This new finding motivates a novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency. Accommodating the system of heterogeneous regressors, TAlasso achieves the well-known oracle property. In contrast, conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously. We apply these LASSO methods to evaluate the short- and long-horizon predictability of S&P 500 excess returns.
appendix boundary found by appendix_command · 68% of the source is main text. Read the extracted text to check this.
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 | Welch, I., Goyal, A (2008) A comprehensive look at the empirical performance of equity premium prediction | 1.000 | 6 | 3 | 100% |
| 2 | Zou, H (2006) The adaptive Lasso and its oracle properties | 1.000 | 5 | 3 | 100% |
| 3 | Xu, K.L (2018) Testing for return predictability with co-moving predictors of unknown form | 0.928 | 4 | 4 | 100% |
| 4 | Medeiros, M.C., Mendes, E.F (2016) $l_1$-regularization of high-dimensional time-series models with non-gaussian and heteroskedastic errors | 0.843 | 3 | 3 | 100% |
| 5 | Tibshirani, R (1996) Regression shrinkage and selection via the Lasso | 0.811 | 4 | 2 | 100% |
| 6 | Koo, B., Anderson, H.M., Seo, M.H., Yao, W (2020) High-dimensional predictive regression in the presence of cointegration | 0.737 | 3 | 2 | 100% |
| 7 | Caner, M., Knight, K (2013) An alternative to unit root tests: Bridge estimators differentiate between nonstationary versus stationary models and select opt… | 0.644 | 2 | 2 | 100% |
| 8 | Hirano, K., Wright, J.H (2017) Forecasting with model uncertainty: Representations and risk reduction | 0.644 | 2 | 2 | 100% |
| 9 | Kock, A.B (2016) Consistent and conservative model selection with the adaptive LASSO in stationary and nonstationary autoregressions | 0.644 | 2 | 2 | 100% |
| 10 | Kostakis, A., Magdalinos, T., Stamatogiannis, M.P (2014) Robust econometric inference for stock return predictability | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.