I am trying to determine the factors driving bitcoin prices. I have a time series set of data with my dependent variable as bitcoin prices (denominated in USD). I have a set of explanatory variables including bitcoin-related metrics as well as non-bitcoin metrics. following some research on time series and data transformation, I understood I needed to transform my dependent variable with the Box-Cox method, which I did.

I am now confused on which transformation I need to perform on my independent variables. Do I also need to perform Box-Cox transformations?

Or maybe did I get it completely wrong ?

For info, I am using XL stat.

  • $\begingroup$ Without knowing why you are considering transforming this is going to be rather hard to answer. $\endgroup$ – mdewey Oct 26 '17 at 14:46
  • $\begingroup$ Sorry for the lack of precision. I am trying to make the data stationary and normal to run a linear regression $\endgroup$ – Tomque Oct 26 '17 at 14:54
  • $\begingroup$ For an indication of one way to approach this problem, see stats.stackexchange.com/a/35717/919. $\endgroup$ – whuber Oct 26 '17 at 16:23
  • $\begingroup$ @Tomque Why would making the data appear normal be required for regression? $\endgroup$ – Glen_b Oct 27 '17 at 4:36
  • $\begingroup$ @Glen_b, I am not that knowledgeable in statistics and the only regressions I made using time series were linear ones which required normality in the data $\endgroup$ – Tomque Oct 27 '17 at 9:38

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