Hi
I've fitted a VECM model in R, and converted in to a VAR representation. I would like to use this model to predict the future value of a response variable based on different scenarios for the explanatory variables.
Here is the code for the model:
library(urca)
library(vars)
input <-read.csv("data.csv")
ts <- ts(input[16:52,],c(2000,1),frequency=4)
dat1 <- cbind(ts[,"dx"], ts[,"u"], ts[,"cci"],ts[,"bci"],ts[,"cpi"],ts[,"gdp"])
args('ca.jo')
vecm <- ca.jo(dat1, type = 'trace', K = 2, season = NULL,spec="longrun",dumvar=NULL)
vecm.var <- vec2var(vecm,r=2)
Now what I would like do is to predict "dx" into the future by varying the others. I am not sure if something like "predict dx if u=30,cpi=15,bci=50,gdp=..." in the next period would work. So what I have in mind is something along the lines of: increase "u" by 15% in the next period (which would obviously impact on all the other variables as well, including "dx") and predict the impact into the future.
Also, I am not sure if the "vec2var" step is necessary, so please ignore it if you think it is redundant.
Thanks
Karl