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

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