摘要翻译:
提出了一种多变量分数驱动模型,用于从含噪向量过程中提取信号。通过假设多元Student's\emph{t}分布的条件位置向量随时间变化,我们构造了一个鲁棒滤波器,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,更广泛地说,它能够克服相依非高斯时间序列向量的问题。我们推导了平稳性和可逆性的条件,并用极大似然(ML)估计了未知参数。证明了该估计量的强相合性和渐近正态性,并通过Monte-Carlo研究说明了该估计量的有限样本性质。从计算的角度出发,导出了解析公式,从而支持了基于Fisher评分法的估算方法的发展。该理论得到了一个新的经验说明的支持,说明了该模型如何有效地应用于从家庭扫描仪数据估计消费价格。
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英文标题:
《A Robust Score-Driven Filter for Multivariate Time Series》
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作者:
Enzo D'Innocenzo, Alessandra Luati, Mario Mazzocchi
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最新提交年份:
2021
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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英文摘要:
A multivariate score-driven model is developed to extract signals from noisy vector processes. By assuming that the conditional location vector from a multivariate Student's \emph{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.
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PDF链接:
https://arxiv.org/pdf/2009.01517