Hi there,

I want to need a method for inference with variable selection.
To do so, I tried the dsregress command to apply lasso variable selection and regression.

The command of dsregress generally reads as
Code:
dsregress depvar varsofinterest, controls([(alwaysvars)] othervars)
Assume I have dependent variable Y, independent variables X1-X50 which are supposed to be always included in the model. And, finally, I have Z1-Z100 which are optional variables to be selected or excluded by lasso.

I was wondering where the conceptual difference lies between varsofinterest and alwaysvars? From my understanding, both sets of variables are treated identically from a computational perspective. Only the produced output is different.

However, if I run
Code:
 dsregress Y X1-X50, controls(Z1-Z100) sel(cv)
I obtain a different set of selected variables than for
Code:
 dsregress Y X1, controls((X2-X50) Z1-Z100) sel(cv)
Both lines of code are of course based on the same seed.

As I interpret the dsregress command, both approaches should always include X1-X50 and select among Z1-Z100.
However, there seems to be a difference between both lines.

Can anybody clarify on this? Thank you!