I am trying to do a box-cox transformation with swift. I have a dependent variable, annual foreign sales of companies (in US\$ thousands) which contains zeros, for a set of panel data. I have been advised to add a small amount, for example, 0.00001 to the annual foreign sales figures so that I can take the log, but I think box-cox transformation will produce a more appropriate constant than 0.00001. I have done a box-cox transformation on R with the codes below, but it has given me a very large lambda2 of 31162.8.
library(geoR) boxcoxfit(bornp$ForeignSales, lambda2 = TRUE) #R output - Fitted parameters: # lambda lambda2 beta sigmasq # -1.023463e+00 3.116280e+04 9.770577e-01 7.140328e-11
My hunch is that the above value of lambda2 is very large, so I am not sure if I need to run the boxcoxfit with my independent variables like below:
boxcoxfit(bornp$ForeignSales, bornp$family bornp$roa bornp$solvencyratio,lambda2=TRUE)
I am still trying to identify the best set of independent variables, so I am not sure if using the boxcoxfit with independent variables at this stage will work or is best.
Here's the description of the two lambda parameters from the help:
lambda numerical value(s) for the transformation parameter $\lambda$. Used as the initial value
lambda2 logical or numerical value(s) of the additional transformation (see DETAILS
TRUE this parameter is also estimated and the initial