D1-3 Fuzzy Cluster Analysis with Mixed Frequency Data

Abstract

Time series are often sampled at different frequencies like month, quarter, etc. The classic fuzzy cluster analysis simply aggregates all data into the common lowest frequency and then computes a similarity matrix. Such temporal aggregation may yield inaccurate or misleading results due to information loss. Inspired by the growing literature of Mixed Data Sampling (MIDAS) regression technique, this paper proposes a way to construct a similarity matrix exploiting all data available whatever their sampling frequencies are. In empirical application on recent Japanese and U.S. macroeconomic indicators, the MIDAS approach and the classic low frequency approach produce different partition trees.