Hi, I'm hoping to get some help on how to manually tweak up some source code to apply to an SVAR Model using the vars
dataset. Here is a snippet of the dataset I'm using
data <- structure(c(1.54054238132604, 5.22775636647117, 1.75154671443607,
-4.00396437623925, -4.99849595390862, -3.58991362838901, -2.08646214793927,
4.38408682550573, -1.32609419137286, -10.0477865139619, -10.5789067079456,
3.53586648125426, 3.80285045230506, 4.54214390385599, 5.25998848716807,
4.53265591355598, 4.18968593558051, 3.53167443997122, 2.68286514861102,
2.28625948262682, 1.9539414753903, 2.30401026783675), .Dim = c(11L,
2L), .Dimnames = list(NULL, c("GDP", "CPI")), index = structure(c(1514764800,
1522540800, 1530403200, 1538352000, 1546300800, 1554076800, 1561939200,
1569888000, 1577836800, 1585699200, 1593561600), tzone = "UTC", tclass = "Date"), class = c("xts",
"zoo"))
and the code for the model
library(vars)
var.model <- vars::VAR(data, p=1, type = 'cons')
svar.model <- vars::BQ(var.model)
I'm now looking to do a historical decomposition of the cumulative effects from shocks on each variable in my model. I've found a solution on stackoverflow (see here) which can be run on a varest
object, however it gives the following error when run on an svarest
object
historical_decomposition <- VARhd(Estimation=svar.model)
Error in (lags + 1):nrow(DATA) : argument of length 0
Any ideas on how to solve this issue? TIA