Multivariate classification of business phases - Claus Weihs; Michael C. Röhl; Winfried Theis (Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, 1999)
We propose multivariate classification as a statistical tool to describe business cycles. These cycles are often analyzed as a univariate phenomenon in terms of GNP or industrial net production ignoring additional information in other economic variables. Multivariate classification overcomes these limitations by reducing dimension in a way suitable for human perception. Based on a four phase scheme (upswing, upper turning point, downswing, lower turning point) we demonstrate the potential of classification methods by determining the important economic variables (stylized facts) for the German business cycle.
Keywords: 519; Business cycle; classification; dimension reduction; simulated annealing; transition structure; Konjunktur; Zeitreihenanalyse; Multivariate Analyse; Theorie