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Benchmarking, Temporal Disaggregation, and Reconciliation of Systems of Time Series

机译：时间序列系统的基准测试，时间分解和协调

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摘要

Most applications of economicmodels to real-world issues must deal with the problem of extracting results based on data, with noise. Data are often interconnected with economic or accounting relationships. However, due to errors in the economic data or the nature of the statistical procedures used to process the data, some (but not necessarily) linear accounting restrictions, which should be satisfied, are not met. This happens, for example, because economic data are frequently collected with different methods using different sample surveys or different pieces of measuring equipment. Many applied fields are impacted by this problem: during the complex production process of national accounts and balance of payments, data are often incomplete at some level of disaggregation. A consequence of this is that there could be different estimates of the same variable or, more generally, some expected linear restrictions on the data are not satisfied. A question arises from such situations is how to use this information efficiently to produce fit-to-use estimates of the variables.
机译：经济模型在现实世界中的大多数应用都必须处理基于数据，带有噪声的结果提取问题。数据通常与经济或会计关系相互关联。但是，由于经济数据中的错误或用于处理数据的统计程序的性质，未满足应满足的一些（但不一定）线性会计限制。例如，发生这种情况是因为经常使用不同的方法，使用不同的样本调查或不同的测量设备来收集经济数据。许多应用领域都受到此问题的影响：在国民账户和国际收支的复杂生产过程中，数据在某种程度的分解中常常是不完整的。这样做的结果是，可能会对同一变量进行不同的估计，或者更普遍地，对数据的某些预期线性限制得不到满足。这种情况引起了一个问题，即如何有效地使用此信息来生成变量的适合使用的估计。

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  • 来源
    《Statistica neerlandica》 |2018年第4期|402-405|共4页
  • 作者

    Baoline Chen; Tommaso Di Fonzo; Nino Mushkudiani;

  • 作者单位

    Bureau of Economic Analysis, U.S. Department of Commerce,Washington, District of Columbia;

    Department of Statistical Sciences, University of Padua, Padua, Italy;

    Centraal Bureau voor de Statistiek, The Hague, The Netherlands;

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