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首页> 外文期刊>Journal of Time Series Analysis >A New State-space Methodology To Disaggregate Multivariate Time Series
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A New State-space Methodology To Disaggregate Multivariate Time Series

机译：分解多元时间序列的新状态空间方法

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摘要

This article addresses the problem of disaggregating multivariate time series sampled at different frequencies using state-space models. In particular, we consider the relation between the high-frequency and low-frequency models, the possible loss of observability and identifiability in the latter with respect to the former, the estimation of the parameters of the low-frequency model by maximum likelihood, and the prediction and interpolation of high-frequency figures when only low-frequency data are available. Since vector autoregressive moving average models are a special case of state-space models, our results are also valid for those models, but they include other models as well, like structural models. We provide a rigorous theoretical development of the aforementioned issues, including a comparison with the classical model-based approaches, and we propose a practical methodology to disaggregate multivariate time series that is both efficient and easy to implement.
机译：本文解决了使用状态空间模型分解以不同频率采样的多元时间序列的问题。特别是，我们考虑了高频和低频模型之间的关系，后者相对于前者的可观察性和可识别性的可能损失，通过最大似然估计低频模型的参数，以及当只有低频数据可用时，对高频图形进行预测和内插。由于向量自回归移动平均模型是状态空间模型的特例，因此我们的结果对于这些模型也有效，但它们也包括其他模型，例如结构模型。我们对上述问题进行了严格的理论发展，包括与基于经典模型的方法进行了比较，并且我们提出了一种实用的方法来分解多元时间序列，既高效又易于实现。

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