I have a set of possible candidates that I want to use in a multivariate regression. I am trying to reduce this set by the following procedure (using Stata):
Step 1: univariate regression (if significant Step 2 follows)
Step 2: regression using controls (if significant Step 3 follows)
Step 3: check certain hurdle rate (e.g. t>3 or p<0.05)
Step 4: group variables according to what they are supposed to measure, e.g. there are some variables that are supposed to measure macroeconomic state (so I suppose they are potentially related).
Step 5: Regress dependend var on independent var of a group, checking for multicollinearity and significance.
Step 6: use survivors of different groups for final multivariate regression.
Step 7: test multivariate regression in distinct sample period
By doing so, I will loose some promising variables as they might be only significant in combination with other variables. Do you have any hints if this is anyways a feasible approach or is there a more adequate approach out there that I could not find yet?
Many thanks for your answers, I hope that's a valid question and I gave enough input. If not, please let me know.
Best
Juliett