Functional-coefficient quantile cointegrating regression with stationary covariates

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2024-12-31 DOI:10.1016/j.spl.2024.110344

Haiqi Li , Jing Zhang , Chaowen Zheng

引用次数: 0

Abstract

This study examines the estimation and inference of functional-coefficient quantile cointegrating regression. Firstly, a local linear quantile regression estimator is proposed to estimate the unknown coefficient function. Secondly, to alleviate the endogeneity problem, we propose a nonparametric fully-modified quantile regression estimator that is shown to be

n \sqrt{h}

consistent and follow a mixed normal distribution asymptotically. Thirdly, we propose two Kolmogorov–Smirnov type test statistics for coefficient stability in a given quantile or across multiple quantile levels. Finally, to improve the finite sample performance, we propose a fixed regressor wild bootstrap procedure and establish its asymptotic validity. Monte Carlo simulation results confirm the merits of the proposed estimator and tests.

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平稳协变量的功能系数分位数协整回归

本研究探讨功能系数分位数协整回归的估计与推论。首先，提出了一种局部线性分位数回归估计器来估计未知系数函数。其次，为了缓解内质性问题，我们提出了一种非参数全修正分位数回归估计量，它被证明是nh一致的，并且渐近地服从混合正态分布。第三，我们提出了在给定分位数或跨多个分位数水平上的系数稳定性的两个Kolmogorov-Smirnov型检验统计量。最后，为了提高有限样本的性能，我们提出了一个固定回归量的野生自举过程，并建立了它的渐近有效性。蒙特卡罗仿真结果验证了所提估计器的优点和测试结果。

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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