arXiv 19 Oct 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2410.15097 · PDF · DOI · OpenAlex · Extracted main text
This paper advances a variable screening approach to enhance conditional quantile forecasts using high-dimensional predictors. We have refined and augmented the quantile partial correlation (QPC)-based variable screening proposed by Ma et al. (2017) to accommodate $\beta$-mixing time-series data. Our approach is inclusive of i.i.d scenarios but introduces new convergence bounds for time-series contexts, suggesting the performance of QPC-based screening is influenced by the degree of time-series dependence. Through Monte Carlo simulations, we validate the effectiveness of QPC under weak dependence. Our empirical assessment of variable selection for growth-at-risk (GaR) forecasting underscores the method's advantages, revealing that specific labor market determinants play a pivotal role in forecasting GaR. While prior empirical research has predominantly considered a limited set of predictors, we employ the comprehensive Fred-QD dataset, retaining a richer breadth of information for GaR forecasts.
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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 | Shujie Ma, Runze Li, and Chih-Ling Tsai (2017) Variable screening via quantile partial correlation | 0.896 | 32 | 6 | 72% |
| 2 | Tobias Adrian, Nina Boyarchenko, and Domenico Giannone (2019) Vulnerable growth | 0.874 | 7 | 2 | 100% |
| 3 | Michael McCracken and Serena Ng (2020) Fred-qd: A quarterly database for macroeconomic research | 0.874 | 5 | 2 | 100% |
| 4 | Kashif Yousuf (2018) Variable screening for high dimensional time series | 0.811 | 4 | 2 | 100% |
| 5 | Christian Brownlees and Andre BM Souza (2021) Backtesting global growth-at-risk | 0.737 | 3 | 2 | 100% |
| 6 | Mikkel Plagborg-Mller, Lucrezia Reichlin, Giovanni Ricco, and Thomas… (2020) When is growth at risk? | 0.737 | 3 | 2 | 100% |
| 7 | Guodong Li, Yang Li, and Chih-Ling Tsai (2015) Quantile correlations and quantile autoregressive modeling | 0.644 | 2 | 2 | 100% |
| 8 | Miguel Angel Arcones and Bin Yu (1994) Central limit theorems for empirical and u-processes of stationary mixing sequences | 0.511 | 2 | 2 | 50% |
| 9 | Bin Yu (1994) Rates of convergence for empirical processes of stationary mixing sequences | 0.511 | 2 | 2 | 50% |
| 10 | Jianqing Fan and Jinchi Lv (2008) Sure independence screening for ultrahigh dimensional feature space | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 28 scored citations.