Organizing Team:
Albrecht Ritschl (Humboldt-Universitaet zu Berlin, Germany)
Ulrich Woitek (Universität Zürich, Switzerland)

Among other things, GLOBALEURONET aims to promote research on European market integration, on macroeconomic and financial fluctuations, and on economic growth. This project intends to make contributions to all three areas, building on recent developments in statistical large-scale aggregation of information.

There are two approaches available for quantitative research of aggregate phenomena in economic history. The traditional one, to be labelled New Data Approach, has looked at reconstructed national accounts (RNA) and the information therein. An alternative is to employ large numbers of disaggregate data and to leave the aggregation to a statistical optimisation algorithm. This proposal is about making progress in the second field.

We draw on two different but related methodologies in recent statistical research on business cycle dating. One, based on frequency domain techniques, examines quasi-cycles in time series either national product or related series and examines interrelations between these by means of coherence analysis. The second is based on statistical factor analysis and aims to aggregate the relevant information inherent in large numbers of disaggregate time series, extracting common factors that can themselves be analyzed using time series techniques. Both approaches are being applied successfully in a currently emerging literature on the dating and analysis of historical business fluctuations. We aim to contribute to this research, developing the conceptual tools further and applying them to historical datasets. As the application of this class of statistical techniques to historical data is still quite new, we expect to have to adjust the methodology to meet the specific needs of the historical at hand. Naturally, much of the analysis is to be based on Bayesian techniques, which implies that suitable prior distributions will have to be experimented with.

The aim of this project is twofold. On the one hand, we want to apply dynamic factor analysis (and principal component analysis, which is closely related) to historical data on real output, monetary and financial market indicators. These techniques provide powerful techniques for statistical aggregation even where the raw data do not easily permit construction of national account aggregates. This is often the case with historical data, where the validity of RNA data has frequently been subject to debates and revisions. It may also apply to centrally planned economies where disaggregate information is often available but reliable aggregation according to SNA standards presents difficulties. Statistical aggregation by factor analysis promises to be an attractive alternative to RNA in such cases. It also may help to resolve debates about historical business cycle chronologies (and arguably, also about levels) derived from existing RNA.

The second aim of this project is to bring national information from different countries together to analyze market integration, business cycle co-movement, and international risk sharing. We intend to bring researchers from different national and regional backgrounds together and to draw on their specific knowledge of disaggregate data that may be useful for business cycle dating purposes. An intermediate aim of this project will be to build a disaggregate European database (DISEURODAT) that may serve as a common pool resource, and that we intend to make available to the research community on the web. At the same time, we intend to standardize the software routines developed in the process and to make a Matlab-based collection of applications (EUROFACTORS) available to participants and incoming junior researchers.