A simplified condition for quantile regression

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2025-04-30 DOI:10.1016/j.spl.2025.110444

Liang Peng , Yongcheng Qi

引用次数: 0

Abstract

Quantile regression is effective in modeling and inferring the conditional quantile given some predictors and has become popular in risk management due to wide applications of quantile-based risk measures. When forecasting risk for economic and financial variables, quantile regression has to account for heteroscedasticity, which raises the question of whether the identification condition on residuals in quantile regression is equivalent to one independent of heteroscedasticity. In this paper, we present some identification conditions under three probability models and use them to establish simplified conditions in quantile regression.

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分位数回归的简化条件

分位数回归在建模和推断给定一些预测因子的条件分位数方面是有效的，并且由于基于分位数的风险度量的广泛应用而在风险管理中变得流行。在预测经济和金融变量的风险时，分位数回归必须考虑异方差，这就提出了分位数回归中残差的识别条件是否等同于一个独立于异方差的条件的问题。本文给出了三种概率模型下的识别条件，并用它们建立了分位数回归的简化条件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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