R: IRFs in a SVAR model, can't display specified model

Question

I am doing a SVAR (structural vector auto regression) analysis in which I want to plot IRFs (impulse response functions). My time series have length 137 and I only use 3 variables, furthermore I select 1 lag when specifying the VAR model.
Specifying the VAR model works fine, but when I want to summarize it I get the following error message

VAR_reduced <- VAR(VAR_data_1, p = 1, type = "both")
summary(VAR_reduced)

Error in solve.default(Sigma) : 
  system is computationally singular: reciprocal condition number = 1.03353e-16

From what I read in another question this error usually come up when there are not enough observations leading to overfitting, but in my example this should not be a problem, as I have enough observations.

As R does not display an error message if I don't run the summary command it is still possible to calculate the IRFs using:

plot(irf(VAR_reduced, n.ahead = 40))

But, the plot seems rather counter-intuitive, as there is no reaction from any variable other than assets. Therefore, my guess is that the error message hints at something I got wrong but haven't realised yet.

Is this correct, that is do I need to solve that error, or do my IRFs have nothing to do with this?

For completeness here is all the code:

library(quantmod)
library(urca)
library(vars)
library(tseries)
getSymbols('CPILFESL',src='FRED')
getSymbols('INDPRO',src='FRED')
getSymbols('WALCL',src='FRED')
CPI <- ts(CPILFESL, frequency = 12, start = c(1957,1))
output <- ts(INDPRO, frequency = 12, start = c(1919,1))
assets <- as.xts(WALCL)
assets <- to.monthly(assets, indexAt='yearmon', drop.time = TRUE)
assets <- ts(assets[,4], frequency = 12, start = c(2002,12))
assets <- window(assets, start = c(2008,9), end = c(2020,1))
CPI <- window(CPI, start = c(2008,9), end = c(2020,1))
output <- window(output, start = c(2008,9), end = c(2020,1))
loutput <- log(output)
lCPI <- log(CPI)
data_0 <- cbind(loutput, lCPI, assets)
plot(data_0)
VAR_data_1 <- ts.intersect(diff(loutput), diff(lCPI), diff(assets, differences = 2))
VAR_reduced <- VAR(VAR_data_1, p = 1, type = "both")
summary(VAR_reduced)