Business Cycle Measurement with Semantic Filtering: A Micro Data Approach

KOF Swiss Economic Institute Working Paper No. 212

17 Pages Posted: 11 Dec 2008

See all articles by Christian Müller-Kademann

Christian Müller-Kademann

Jacobs University

Eva M. Kberl

ETH Zürich - Swiss Institute for Business Cycle Research

Date Written: November 1, 2008

Abstract

In this paper we develop a business cycle measure that can be shown to have excellent ex-ante forecasting properties for GDP growth. For identifying business cycle movements, we use a semantic approach. We infer nine different states of the economy directly from firms' responses in business tendency surveys. Hence, we can identify the current state of the economy. We therewith measure business cycle fluctuations. One of the main advantages of our methodology is that it is a structural concept based on shock identification and therefore does not need any - often rather arbitrary - statistical filtering. Futhermore, it is not subject to revisions, it is available in real-time and has a publication lead to official GDP data of at least one quarter. It can therefore be used for one quarter ahead forecasting real GDP growth.

Keywords: business cycle measurement, semantic cross validation, shock identification

JEL Classification: E32, C4, C5

Suggested Citation

Müller-Kademann, Christian and Köberl, Eva Maria, Business Cycle Measurement with Semantic Filtering: A Micro Data Approach (November 1, 2008). KOF Swiss Economic Institute Working Paper No. 212. Available at SSRN: https://ssrn.com/abstract=1313708 or http://dx.doi.org/10.2139/ssrn.1313708

Christian Müller-Kademann (Contact Author)

Jacobs University

Campus Ring 1
Research V, Room 40
Bremen, 28759
Germany

HOME PAGE: http://www.s-e-i.ch

Eva Maria Köberl

ETH Zürich - Swiss Institute for Business Cycle Research ( email )

Weinbergstrasse 35
CH-8092 Zurich
Switzerland
+41 44 632 50 90 (Phone)

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