EuroMInd-C: A Disaggregate Monthly Indicator of Economic Activity for the Euro Area and Member Countries

C. Frale, S. Grassi, Massimiliano Marcellino, G. Mazzi, Tommaso Proietti
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引用次数: 15

Abstract

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.
EuroMInd-C:欧元区及其成员国经济活动的月度分类指标
本文讨论了对欧元区及其最大成员国的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连锁量的国内生产总值(gdp)进行比较来确定的,后者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有关的大量(及时)月度指标时间序列来实现的,提供了或多或少完整的覆盖范围。指标采用大规模参数因子模型建模。我们讨论了它的规格,并提供了统计处理的细节。计算效率对于我们应用中使用的尺寸的大规模参数因子模型的估计是至关重要的(考虑到大约170个系列)。为了实现这一点,我们应用了最先进的状态空间方法,这些方法可以处理时间聚合和任何缺失值的模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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