EuroMInd-C: A Disaggregate Monthly Indicator of Economic Activity for the Euro Area and Member Countries
EuroMInd-C: A Disaggregate Monthly Indicator of Economic Activity for the Euro Area and Member Countries
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EuroMInd-C：欧元区及其成员国经济活动分类月度指标
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Tommaso Proietti
中科院分区：
文献类型：
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作者：
C. Frale;S. Grassi;Massimiliano Marcellino;G. Mazzi;Tommaso Proietti
本文涉及对欧元区及其最大成员国经济活动月度指标的估计，这些指标具有以下属性：相关性、代表性和及时性。相关性是通过将我们的月度指标与环比国内生产总值进行比较来确定的，这是衡量经济活动水平的最重要指标。代表性是通过考虑与经济活动水平相关的大量（及时）月度指标时间序列来实现的，从而提供或多或少的完整覆盖。这些指标使用大规模参数因子模型进行建模。我们讨论其规范并提供统计处理的详细信息。计算效率对于估计我们应用中使用的维度的大规模参数因子模型（考虑大约 170 系列）至关重要。为了实现这一目标，我们应用了最先进的状态空间方法，可以处理时间聚合和任何缺失值模式。
This paper deals with the estimation of monthly indicators of economic activity for the Euro area and its largest member countries that possess the following attributes: relevance, representativeness and timeliness. Relevance is determined by comparing our monthly indicators to the gross domestic product at chained volumes, as the most important measure of the level of economic activity. Representativeness is achieved by considering a very large number of (timely) time series of monthly indicators relating to the level of economic activity, providing a more or less complete coverage. The indicators are modelled using a large-scale parametric factor model. We discuss its specification and provide details of the statistical treatment. Computational efficiency is crucial for the estimation of large-scale parametric factor models of the dimension used in our application (considering about 170 series). To achieve it, we apply state-of-the-art state space methods that can handle temporal aggregation, and any pattern of missing values.