We know that correlation and cointegration are two different things. The question that I want to put here and share with you is whether this is true even when we consider the wavelet concept?
dear that can't be used ...
there is recent study which proposes wavelet based cointegration test ...
It is merely a scale-localized version of the usual cross-correlation between two signals. In cross-correlation, you determine the similarity between two sequences by shifting one relative to the other, multiplying the shifted sequences element by element, and summing the result.
Wavelet cannot be used to test for cointegration
Welcome all...
To answer this question, let us differentiate between the correlation that may exist between an independent variable and a dependent variable, or between the same variable for different periods of time. As for the issue of cointegration, it is a different type of correlation between two time series. The two chains may suffer from imbalance if we take each chain separately. But if we take these two series together and one of them corrects the deviation between them such that one cancels the deviation of the other, this means the existence of cointegration. It is an indication of the existence of a long-term relationship between variables... Finally, this series may be linear or non-linear, and depending on the nature of the series, appropriate standard models can be used.
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