The present article features a hierarchical Bayes method applied to solving problems of benchmarking and contemporaneous reconciliation across time series. This method enables the use of high frequency series to be either approximations or one or several related indicators. This method may be applied when facing flow or index disaggregation problems. The authors compare their results to classical procedures (viz., Denton univariate and Rossi multivariate methods) through the use of indicators. This article concludes that the suggested method bestows greater importance on the low frequency series profile, consequently providing smoother solutions than its counterparts.

中文翻译：

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时间序列的基准化和对帐

本文介绍了一种分级贝叶斯方法，该方法适用于解决跨时间序列的基准化和同期对帐问题。这种方法使高频序列的使用成为近似值或一个或多个相关指标。当遇到流量或索引分解问题时，可以应用此方法。作者通过使用指标将其结果与经典方法（即Denton单变量和Rossi多元方法）进行了比较。本文的结论是，建议的方法在低频序列轮廓上具有更大的重要性，因此提供了比同类方法更平滑的解决方案。