论文标题
多元时间序列的强大得分驱动过滤器
A Robust Score-Driven Filter for Multivariate Time Series
论文作者
论文摘要
开发了多元分数驱动的滤波器，以从嘈杂的矢量过程中提取信号。通过假设来自多元学生t分布的条件位置向量会随着时间的推移而变化，我们构建了一个可靠的滤波器，该滤波器能够克服在建模重尾现象时自然出现的几个问题，并且通常更重要的是，依赖于非高斯时间序列的矢量。我们得出平稳性和可逆性的条件，并通过最大似然（ML）估算未知参数。证明了估计量的强一致性和渐近正态性，并通过一项蒙特卡洛研究来说明有限的样本特性。从计算的角度来看，得出了分析公式，该公式同意根据Fisher评分方法制定估计程序。该理论得到了一个新颖的经验例证的支持，该图显示了如何有效地将模型应用于家庭扫描仪数据的消费价格。
A multivariate score-driven filter is developed to extract signals from noisy vector processes. By assuming that the conditional location vector from a multivariate Student's t distribution changes over time, we construct a robust filter which is able to overcome several issues that naturally arise when modeling heavy-tailed phenomena and, more in general, vectors of dependent non-Gaussian time series. We derive conditions for stationarity and invertibility and estimate the unknown parameters by maximum likelihood (ML). Strong consistency and asymptotic normality of the estimator are proved and the finite sample properties are illustrated by a Monte-Carlo study. From a computational point of view, analytical formulae are derived, which consent to develop estimation procedures based on the Fisher scoring method. The theory is supported by a novel empirical illustration that shows how the model can be effectively applied to estimate consumer prices from home scanner data.
