Working Paper: CEPR ID: DP2339
Authors: Christophe Croux; Mario Forni; Lucrezia Reichlin
Abstract: This paper proposes a measure of dynamic comovement between (possibly many) time series and names it cohesion. The measure is defined in the frequency domain and is appropriate for processes that are costationary, possibly after suitable transformations. In the bivariate case, the measure reduces to dynamic correlation and is related, but not equal, to the well-known quantities coherence and coherency. Dynamic correlation on a frquency band equals (static) correlation of band-pass filtered series. Moreover, long run correlation and cohesion relate in a simple way to cointegration. Cohesion is useful to study problems of business cycle synchronization, to investigate short-run and long-run dynamic properties of multiple time series, to identify dynamic clusters. We use state income data for the US and GDP data for European nations to provide an empirical illustration focused on the geographical aspects of business cycle fluctuations.
Keywords: Business Cycle; Comovements; Coherence; Geography
JEL Codes: C10; E3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| dynamic correlation on a frequency band (C32) | static correlation of bandpass filtered series (C10) |
| economic interdependencies (F02) | output fluctuations (E39) |
| long-run dynamic correlation (C22) | stochastic cointegration (C32) |
| geographical proximity (R12) | level of output synchronization (C69) |
| borders (F55) | economic synchronization (F42) |