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Temporal disaggregation by state space methods: Dynamic regression methods revisited

机译：通过状态空间方法进行时间分解：重新探讨了动态回归方法

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The paper advocates the use of state space methods to deal with the problem of temporal disaggregation by dynamic regression models, which encompass the most popular techniques for the distribution of economic flow variables, such as Chow-Lin, Fernandez and Litterman. The state space methodology offers the generality that is required to address a variety of inferential issues that have not been dealt with previously. The paper contributes to the available literature in three ways: (ⅰ) it concentrates on the exact initialization of the different models, showing that this issue is of fundamental importance for the properties of the maximum likelihood estimates and for deriving encompassing autoregressive distributed lag models that nest exactly the traditional disaggregation models; (ⅱ) it points out the role of diagnostics and revisions histories in judging the quality of the disaggregated estimates and (ⅲ) it provides a thorough treatment of the Litterman model, explaining the difficulties commonly encountered in practice when estimating this model.
机译：本文提倡使用状态空间方法通过动态回归模型来处理时间分解问题，其中包括最流行的经济流量变量分配技术，例如Chow-Lin，Fernandez和Litterman。状态空间方法论提供了解决以前没有处理过的各种推理问题所需的通用性。本文通过以下三种方式为现有文献做出了贡献：（ⅰ）着眼于不同模型的精确初始化，表明该问题对于最大似然估计的性质以及推导包含以下各项的自回归分布滞后模型具有根本的重要性完全嵌套传统的分解模型； （ⅱ）指出诊断和修订历史在判断分类估计的质量方面的作用；（ⅲ）提供了对Litterman模型的彻底处理，解释了估计该模型时在实践中通常遇到的困难。

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