I am trying to understand impact of Global Liquidity using times series data (quarterly) for 20 years. Some of the variables in the data (such as GDP, Broad Money Supply M3, Net Capital Inflows as % of GDP) are not stationary or rather I(1). I checked for co-integration using johansen test and found that there exists 2 co-integrating relationship between 5 variables.
My adviser told me to go for VECM rather than SVAR , because a) the model would be correctly specified and b) VECM allows for both short run and long run analysis c) Interpretation of results are simple yet intuitive.
However, when I went though literature, most of the studies (Kim-2001, Sausa and Zhagini-2004, Ruffer and straca 2006) have used SVAR for the same (under the same circumstances). When I asked the same to one more professor, he said "Since your goal is policy analysis (IRF & FEVD), you dont have to worry about non-stationarity, and you can go ahead with SVAR. You can run SVAR with both I(1) and I(0) variables in the model. Not adding co-integrating term would make you loose efficiency, but would not affect the forecasting or Impulse responses." I understood his point but could not understand WHY ?
So I have following two questions, a) Why SVAR is not a mis-specified model, when my variables are co-integrated at levels? or Why, not including co-integration term would not affect IRF or my results?
b) Does running SVAR with I(1) and I(0) leads to model mis-specification?
Understanding these problems would help me immensely to solve the jig-saw puzzle, I am currently find myself in.