The 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 obtained by referring our monthly indicators to gross domestic product at chained volumes, the most important measure of the level of economic activity. Representativeness is achieved by entertaining a very large number of (timely) time series on monthly indicators relating to the level of economic activity, providing a more or less complete coverage. The indicators are modelled with a large scale parametric factor model. We discuss its specification and provide details on the statistical treatment. Computational efficiency is crucial for estimating a large scale parametric factor model of the dimension considered 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.
Index of coincident indicators Temporal Disaggregation Multivariate State Space Models Dynamic factor Models Quarterly National accounts