Tomohiro Ando, Tadao Hoshino, Ruey Tsay
arXiv 8 Jan 2026 · Statistics — Methodology
arXiv:2601.04663 · PDF · DOI · OpenAlex · Extracted main text
This paper considers estimation and model selection of quantile vector autoregression (QVAR). Conventional quantile regression often yields undesirable crossing quantile curves, violating the monotonicity of quantiles. To address this issue, we propose a simplex quantile vector autoregression (SQVAR) framework, which transforms the autoregressive (AR) structure of the original QVAR model into a simplex, ensuring that the estimated quantile curves remain monotonic across all quantile levels. In addition, we impose the smoothly clipped absolute deviation (SCAD) penalty on the SQVAR model to mitigate the explosive nature of the parameter space. We further develop a Bayesian information criterion (BIC)-based procedure for selecting the optimal penalty parameter and introduce new frameworks for impulse response analysis of QVAR models. Finally, we establish asymptotic properties of the proposed method, including the convergence rate and asymptotic normality of the estimator, the consistency of AR order selection, and the validity of the BIC-based penalty selection. For illustration, we apply the proposed method to U.S. financial market data, highlighting the usefulness of our SQVAR method.
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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 | He, Xuming and Wang, Lan and Hong, Hyokyoung G (2013) Quantile-Adaptive Model-Free Variable Screening for High-Dimensional Heterogeneous Data | 0.874 | 7 | 2 | 100% |
| 2 | Ando, Tomohiro and Li, Ker-Chau (2025) Simplex quantile regression without crossing self | 0.874 | 6 | 2 | 100% |
| 3 | Ando, Tomohiro and Bai, Jushan and Lu, Lina and Vojtech, Cindy M (2024) Scenario-based quantile connectedness of the US interbank liquidity risk network self | 0.737 | 3 | 2 | 100% |
| 4 | Bondell, Howard D and Reich, Brian J and Wang, Huixia (2010) Noncrossing quantile regression curve estimation | 0.644 | 2 | 2 | 100% |
| 5 | Fan, Jianqing and Li, Runze (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.644 | 2 | 2 | 100% |
| 6 | Pesaran, H Hashem and Shin, Yongcheol (1998) Generalized impulse response analysis in linear multivariate models | 0.644 | 2 | 2 | 100% |
| 7 | Xiaohong Chen (2007) Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models | 0.511 | 2 | 1 | 100% |
| 8 | Fan, Jianqing and Lv, Jinchi (2008) Sure Independence Screening for Ultra-High Dimensional Feature Space | 0.405 | 1 | 1 | 100% |
| 9 | Lee, Dong Jin and Kim, Tae-Hwan and Mizen, Paul (2021) Impulse response analysis in conditional quantile models with an application to monetary policy | 0.405 | 1 | 1 | 100% |
| 10 | Ando, Tomohiro and Greenwood-Nimmo, Matthew and Shin, Yongcheol (2022) Quantile connectedness: modeling tail behavior in the topology of financial networks self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 31 scored citations.