I am using Stata 13 to estimate a simple model with interaction terms. To give the coefficients a meaningful interpretation at zero, and to avoid multicollinearity, I am mean centering variables.
I am wondering when to do this. I.e. before estimating a regression or only for values that enter the regression? The question stems from the missing structure of my data. Because the mean of the centered variable is not zero when calculated for the observations that acctually entered the regession.
Maybe an example helps in making the point:
clear set more off sysuse auto.dta *Randomly replace weight with missings gen tomis = ceil(10*runiform()) replace weight=. if tomis==1 *Center mpg sum mpg, meanonly gen cmpg = mpg-r(mean) *Regression qui reg price cmpg weight foreign qui gen sample = 1 if e(sample) *Center mpg when in sample sum mpg if sample==1, meanonly gen cmpgs= mpg-r(mean) *Sums sum mpg cmpg cmpgs sum mpg cmpg cmpgs if sample==1
In the example above I mean center
cmpg. The mean of
cmpg is thus (close to) zero. However the mean of
cmpg is 0.278 for all observations that entered the regression. Does that make sense or should I center based on the observation that enter the regression as I do when generated