New Eurocoin: Tracking Economic Growth in Real Time

Abstract

Removal of short-run dynamics from a stationary time series to isolate the medium to long-run component, can be obtained by a band-pass filter. However, band pass filters are infinite moving averages and can therefore deteriorate at the end of the sample. This is a well-known result in the literature isolating the business cycle in integrated series. We show that the same problem arises with our application to stationary time series. In this paper we develop a method to obtain smoothing of a stationary time series by using only contemporaneous values of a large dataset, so that no end-of-sample deterioration occurs. Our construction is based on a special version of Generalized Principal Components, which is designed to use leading variables in the dataset as proxies for missing future values in the variable of interest. Our method is applied to the construction of New Eurocoin, an indicator of economic activity for the euro area. New Eurocoin is an estimate, in real time, of the medium to long-run component of the euro area GDP growth, which performs equally well within and at the end of the sample. As our dataset is monthly and most of the series are updated with a short delay, we are able to produce a monthly, real-time indicator. An assessment of its performance as an approximation of the medium to long-run GDP growth, both in terms of fitting and turning-point signaling, is provided.Coincident Indicator, Band-pass Filter, Large-dataset Factor Models, Generalized Principal Components