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Cross spectral analysis is a statistical technique used to examine the relationship between two time series by analyzing their frequency components. It focuses on how the spectral density of one signal correlates with that of another, allowing researchers to identify shared frequencies and potential causal relationships. This method is particularly useful in fields such as signal processing, economics, and neuroscience, where understanding interactions between different signals is crucial. By employing tools like the cross-spectral density function, it enables the identification of phase relationships and coherence between the two signals across various frequencies.

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