Time Series - Prewhiten

nopon649 — Fri, 08 Jun 2018 06:21:44 GMT

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?

Background:

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,
nopon649

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Time Series - Prewhiten