How to compute confidence intervals for scaled impulses

[RK2509]

Hi Tom,
I am working on a FAVAR model. I am studying the effect of exchange rate shocks on US economy using 2 step PCA, and identification based on recursive ordering of variables.
If you see my code attached, I have first grouped variables into similar variables. There are 5 groups –economic activity, prices, interest rate, money supply, and exchange rate. Economic activity has 95 variables; for prices there 23 variables, finally for money supply there are 7 variables. For each of these groups I extract the first principal component.
In step 2, I estimate the VAR using recursive ordering – economic activity (factor used), prices (factor used), federal funds rate (observable), money supply (factor used), and exchange rate (observable). After estimation, I accumulate the impulses and then I use the factor loadings to compute the impulse responses for all the variables at the disaggregate level. (I am basically scaling them by their factor loadings)
I could compute the confidence intervals for the main 5 variables that enter my VAR using @MCVARDODRAWS and @MCGraphIRF. My question is that how can I compute the confidence intervals for all the variable at the disaggregate level. I have done a scaling there so I am not able to figure out how to have confidence intervals for the scaled responses too.
Could you please help me out. I am not sure if my question is clear. Basically how do I calculate confidence intervals for the responses in a(), c(), and d()
I would be extremely grateful for your help.

Many Thanks

[TomDoan]

I have no idea what you're trying to do with that. Your "lambda"'s are based upon a principal components extraction from the whole set of variables, but then you do subset factors, and do the VAR using those. There is no relationship between the overall PC's and the subset PC's, so I can't understand what you're trying to do applying the original lambda's to the VAR's.