Paper#Time Series Forecasting, Quantile Regression, Prediction Intervals🔬 ResearchAnalyzed: Jan 3, 2026 20:17
Prediction Intervals for Quantile Autoregression
Published:Dec 26, 2025 12:38
•1 min read
•ArXiv
Analysis
This paper introduces novel methods for constructing prediction intervals using quantile-based techniques, improving upon existing approaches in terms of coverage properties and computational efficiency. The focus on both classical and modern quantile autoregressive models, coupled with the use of multiplier bootstrap schemes, makes this research relevant for time series forecasting and uncertainty quantification.
Key Takeaways
- •Introduces new methods for constructing prediction intervals using quantile-based techniques.
- •Applies to both classical and modern quantile autoregressive models.
- •Employs multiplier bootstrap schemes for coefficient estimation and future observation replication.
- •Demonstrates improved coverage properties and computational efficiency compared to existing methods.
- •Validated through simulations and real-world applications (U.S. unemployment rate, retail gasoline prices).
Reference
“The proposed methods yield improved coverage properties and computational efficiency relative to existing approaches.”