Development of a New Multivariate Time Series Analysis Method and Its Application to Business Cycles

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  • 新しい多変量時系列解析手法の開発とその景気循環への応用

Abstract

We regard macroeconomic phenomena such as business cycles and price fluctuations as collective movements of individual agents and approach them empirically using concepts and methods from statistical physics. Following this approach, we have recently developed a Complex Hilbert Principal Component Analysis (CHPCA) method for detecting collective movements in real data sets, which has the same computational complexity as the PCA method based on real data and can be used to easily analyze the dynamic correlation structure among multivariate variables. This analysis method can be used to mechanically extract the lead-lag relationship among basic business trend indicators. That is, there is a possibility that the selection of basic indicators needed to construct leading, co-incident, and lagging business trend indices can be made in a more objective way. We also report the results of our examination of the leading nature of the Business Watchers Survey data with respect to the main body of the economy using the CHPCA method. We find that the data are very promising as leading indicators.

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