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Time Series - Prewhiten


New Contributor


Jan 17, 2018

Hello Everyone,


My question pertains to understanding what the terminology in JMP's prewhiten platform means.

Also, understanding how that helps me determine an appropriate Transfer Function that I can use to test whether x causes y.


JMP's prewhiten output shows three different ACF's labeled "Prewhiten Corr", "Noise AutoCorr", & "Noise Partial AutoCorr".

What is the Prewhiten Corr, Noise AutoCorr, & Noise Partial AutoCorr refer to?

      Is the prewhiten corr the correlation between the residuals of the x's sarima model & filtered y response?

      Is Noise AutoCorr & Noise Partial AutoCorr the noise of the y filtered variable?


Do I use the Noise AutoCorr & Noise Partial AutoCorr to specify the y sarima portion of the Transfer function & use the sarima model for the x input during the prewhiten phase? 



I am interested in utilizing JMP's prewhitening tools to help identify if one of two time series variables are 'causing / changing' the other time series variable.


My class is using R. We fit essentially the sarima model to the x 'input' time series and save the residuals.

Meanwhile, the y 'output' is filtered based on the sarima model fit to x.


Finally, we plot the residauls of x's sarima model versus the filtered y.


I am really trying to figure out how do I get this graph of the x's residuals versus filtered y.

Then, determine if x causes y.


Thank you in advance for your help in this matter,