CAViaR模型推理的自适应随机带宽
Adaptive Random Bandwidth for Inference in CAViaR Models
摘要：
本文研究了CAViaR模型Wald检验的尺寸性能（Engle和Manganelli，2004）。我们发现，通常的估计策略测试统计量产生不准确。事实上，我们表明，现有的密度估计方法不能适应CAViaR模型的条件概率密度的时间变化。因此，我们开发了一种称为自适应随机带宽的方法，该方法可以鲁棒地近似时变条件概率密度，用于基于模型参数估计量的渐近正态性的CAViaR模型的推断测试。该方法还避免了概率密度估计中最优带宽的选择问题，并可直接推广到多元分位数回归中。
英文摘要：
This paper investigates the size performance of Wald tests for CAViaR models (Engle and Manganelli, 2004). We find that the usual estimation strategy on test statistics yields inaccuracies. Indeed, we show that existing density estimation methods cannot adapt to the time-variation in the conditional probability densities of CAViaR models. Consequently, we develop a method called adaptive random bandwidth which can approximate time-varying conditional probability densities robustly for inference testing on CAViaR models based on the asymptotic normality of the model parameter estimator. This proposed method also avoids the problem of choosing an optimal bandwidth in estimating probability densities, and can be extended to multivariate quantile regressions straightforward.
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