Continuous monitoring of the evolution of the economy is fundamental for the decisions of public and private decision makers. The paper proposes EUROMIND, which is a new monthly indicator of the euro area economic conditions, based on tracking real gross domestic product monthly, relying on information provided in the Eurostat Euro-IND database. EURO-MIND has several original economic and statistical features. First, it considers both the output and the expenditure sides of the economy, as it provides a monthly estimate of the value added of the six branches of economic activity and of the main gross domestic product components by type of expenditure (final consumption, gross capital formation and net exports), and combines the estimates with optimal weights reflecting their relative precision. Second, the indicator is based on information at both the monthly and the quarterly level, modelled with a dynamic factor specification cast in state space form. Third, since estimation of the multivariate dynamic factor model with mixed frequency data can be numerically complex, computational efficiency is achieved by implementing univariate filtering and smoothing procedures. Finally, special attention is paid to chain linking and its implications, via a multistep procedure that exploits the additivity of the volume measures expressed at the prices of the previous year.
In the paper we aim to introduce a statistical dating and detection of turning points giving them a first economic interpretation. The main advantage of the proposed approach is represented by the fact that classical and growth cycles are jointly considered both in the dating and in the detecting stage. A key result of this choice is a better description of different economic phases as well as a more accurate investigation of the economic cyclical behaviour. The proposed approach considerably improves the relevance of information delivered to users in comparison with a standard analysis based only on classical or growth cycle components. Copyright � 2008 The Authors. Journal compilation � 2008 Blackwell Publishing Ltd and The University of Manchester.
In this paper we propose a monthly measure for the euro area gross domestic product (GDP) based on a small-scale factor model for mixed-frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short-term forecasting performance of the model in a pseudo-real-time experiment. We find that the survey-based factor plays a significant role for two components of GDP: industrial value added and exports. Moreover, the two-factor model outperforms in terms of out-of-sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single-factor model, with few exceptions. Copyright © 2009 John Wiley & Sons, Ltd.
High frequency data, i.e. data observed at infra-monthly intervals, have been used for decades by statisticians and econometricians in the financial and industrial worlds. Weekly data were already used in the 20's by official statisticians to assess the short-term evolution of the Economy. For example, Crum (1927) studied the series of weekly bank debits outside New York city from 1919 to 1026 and proposed a method to seasonally adjust these data based on the median-link-relative method developed by Persons (1919).
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