Hello,
I am using a dataset that has various variables in currency, values, counts, and percentages. To bring them all to one level I understand I need to take the log values. However, since there are percentages, the dataset has some negative values. Therefore, while taking the log of these variables, my data set has a few NAs. How do I solve this issue? I need to get rid of NAs to run GARCH or any other similar models. What do I do to convert those NAs. I cannot omit the values as I need the dataset to be uniform all along. Also, I cannot take the difference (d(log)) values which will restrict me from taking the 1st value of the dataset.
What can a researcher do in this case?
Is there any other way to bring all values to one level apart from taking a log? or is there a standard procedure to replace the NAs once we get NA after taking log?
Thanks a lot for considering to address this issue.
Regards,
Shreya
Shreya,
Consider applying the Box-Cox transformation to your dataset. This method is versatile, designed to handle various data types, and particularly effective in stabilizing variance while achieving a more normal distribution. The transformation introduces a parameter (lambda) that is estimated from the data, allowing it to handle both zero and negative values. This approach not only addresses the issue of negative values but also provides flexibility in transforming your data. Once transformed, you can seamlessly integrate the new variable into your GARCH or similar models.
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