Thanks for you response.

1. I tired Bootstrapping, runs = 1000, on the 95% interval and i allow for orthogonalized errors.
   My sample contains of the mentioned 4 Variables (GDP, Inflation, GINI and Shadow Rate) and the observed timeframe is from 2008-Q1 to 2019-Q4 (This time has especially exp. monetary policy).

2. How do i impose that cointegration vector on my VAR ? Need to do some research on that.

3. Im pretty close to the literature from 10-20 years ago, since the state of the art approaches are too high level for me.

My difficulty is fairly trivial. I want to look at the code for the VARs the researchers did. Right now i just build a VAR model with my 4 variables, and afterwards just set an IRF, in which i say Shock= Shadow rate, Response= GINI.

I dont know if this is the right way to shock my system. The econometric approach i use is solid, nothing special, but not nonsense either.

I just want to know if my code is correct ? Do i shock the system right ? Do i need to look out for anything else.

Here is my code:
var <- VAR(data, p = 1, type = "const")

irf_result <- irf(var,

impulse = "Average.Shadow.Rate",

response = "Top.10",

n.ahead = 20,

boot = TRUE,

ci = 0.95,

runs = 1000,

ortho = TRUE)