Hi Statalist. I have a conundrum. I have several variables to which I need to apply the log transformation, such as GDP per capita. However, one of those variables - "cumulative experience" (a count of times a firm has manufactured a nuclear reactor prior to the current observation) - has several instances of zeros in the data. Therefore, I am taking the standard advice of using the inverse hyperbolic sine transformation. For all my other variables, there are no zero-valued observations, so the log transformation works fine. Moreover, the untransformed values of these non-problematic variables are large enough that the approximation asinh(x)=ln(x)+ln(2) is effectively true for my data. So it's purely a stylistic choice (as far as I can tell) as to which one I use.

My question is this: should I apply the same transformation (inverse hyperbolic sine) to all the variables that need to be transformed for the sake of consistency? Or would it be okay if I apply the log transformation to the variables that don't have the issue of zero-valued observations? It would be nice if I could refer to "log GDP per capita" when writing and speaking, since that is such a common transformation.

In case it matters, cumulative experience is a count variable, but it is not the outcome of interest. My outcome of interest is continuous. So that's why I'm not using count methods here. Are there even methods for when count data is on the right-hand side of the regression? Is that an issue?