When it comes to practical solutions, finding causal links is one of the holy grails in all disciplines of science. Although Granger-type causality doesn’t prove actual causality, it becomes a powerful tool for analyzing any system once it’s coupled with theoretical reasoning. Recent advances in wavelet analysis as a tool to unravel the patterns and coherence among different phenomena raise the question of whether such coherence can be used to indicate causality.
The 11th annual Austrian Stochastics Days were held on September 7 and 8, 2023, at the University of Klagenfurt. The Austrian Stochastics Days is a forum for researchers working on theoretical or practical topics in stochastic analysis. It covers any topic in stochastics, from insurance models to stochastic system analysis. The IIASA Cooperation and Transformative Governance (CAT) research group's research scholar, Mohammad Reza Yeganegi, shared their findings on the connection between wavelet analysis results and the Granger-type causality test in collaboration with Hossein Hassani, Senior Research Scholar in the IIASA CAT Research Group.
Granger-type causality tests are renowned for their ability to explore relationships between multiple time series. Theoretically, any causal relationship within a dynamic system’s variables will result in a phase difference among those variables. While Granger-type causality does not confirm actual causality, it provides a practical method for evaluating hypothesized causal links. In other words, once a hypothesis has been proposed within the scientific framework of a particular field, it can be empirically tested with observed data using Granger-type causality tests. However, such tests don’t shed light on the details of causal relationships. To address this, researchers have attempted to unify Granger-type causality tests with phase difference analysis. This unification of methods links the practical application of Granger-type causality tests with a deeper understanding of co-existing patterns over time.
In his talk, Yeganegi discussed a wavelet framework to merge these two concepts in stochastic systems, arguing that findings from one approach could enrich the results from the other. Findings from Yeganegi and Hassani's research showed a path to incorporating wavelet analysis and Granger-type causality tests for stochastic system analysis.
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