Cointegration regression for stationary series I have seven variables that are stationary at first difference. I am trying to figure out how variable X influences variable Y, conditional on Z, W, A, B, and C. 
Which would be more appropriate to use? STATA's cointreg package for Fully Modified OLS (FMOLS), or just stick with VAR?
 A: Notice that having all variables at the same degree of stationarity (I(1), or first-difference stationary), does not imply there is a long run relationship among them. The relationship can still be spurious. So, you still need to proceed by testing whether such relationship exists. Different method exists for this. If no relationship exists, results are meaningless with any method you use.
Going to your question, cointreg is a powerful command, which compute robust SE to do testing and gives you the long run cointegration equations in the output, which for economists is a central piece of information when it comes to evaluating theory. If you find evidence of cointegration, your equation is ready to run, as your equation is balanced, in the sense that all the variables are I(1). I see no reason why you would prefer a VAR, where robust SE is not assured. 
I also point you to Stata's egranger package (here), which estimates an error-correction model (ECM), which includes both the long run and the short-run relationship, giving you much more insights. In a multidimensional context, you can estimate a vector ECM, or VECM, using the vec command. For documentation on this and related commands, see this document.
