Using Poisson instead of transforming the data I note with interest an article which has been suggested to me over here:
http://blog.stata.com/tag/poisson-regression/
I have the problems outlined in this blog - namely zeros (in the independent variables).
The article suggests the use of Poisson even though there is no count data in the dependent variable.
What should I be on the look out for in terms of checking that Poisson is suitable for my data and that the results I am obtaining are meaningful?
In previous posts I have already indicated the nature of my data. The dependent variable is investment in millions of pounds, whilst the independent variables include age of firm, and a number of indicators. I have also other regressions to run which include a number of counts in the independent variable which also could be zero.
In my case, investments tend to be skewed towards lower amounts, and so is the age of firm.  There is an issue with using OLS - the normality assumption of residuals does not hold, given the bias towards smaller investments and younger firms.. also there is evidence of heteroskedasticity (ok I can regress with robust standard errors to account for this).  I have tried using mboxcox and the ladder feature to try and identify possible transformations but all efforts till now proved useless.
If I use iqr - I note that i have < 2% mild outliers, and no severe outliers.
Trust you'd be able to help further on this...
 A: That seems bizarre. The Poisson GLM certainly can converge and consistently estimate relative rates even when the outcome is non-integral (for instance, in the case of aggregate data with frequency weights, as is used in binomial regression). Additionally, using robust standard error estimates ensures that the 95% confidence intervals are asymptotically correct, you can tremendously inflate type I error by using a working Poisson probability model for the outcome when there is no mean variance relationship. Doing this for a simply log transformation is insane.
If it were me, I would achieve a log transformation (without log transforming my outcome) using the following syntax:
glm y educ exp exp2, family(gaussian) link(log)

This uses the GLM infrastructure to automatically log transform the outcome, but no idiotic mean-variance relationship is specified. Furthermore,
glm y educ exp exp2, family(gaussian) link(log) vce(robust)

Using the robust variance estimator will ensure that standard error estimates are consistent in the presence of heteroscedasticity.
