title: R: Use VAR model to predict response to change in values of certain variables
body: <p>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.</p>
  
  <p>Here is the code for the model:</p>
  
  <pre><code>library(urca)
  library(vars)
  
  input &lt;-read.csv("data.csv")
  ts &lt;- ts(input[16:52,],c(2000,1),frequency=4)
  dat1 &lt;- cbind(ts[,"dx"], ts[,"u"], ts[,"cci"],ts[,"bci"],ts[,"cpi"],ts[,"gdp"])
  
  args('ca.jo')
  vecm &lt;- ca.jo(dat1, type = 'trace', K = 2, season = NULL,spec="longrun",dumvar=NULL)
  vecm.var &lt;- vec2var(vecm,r=2)    
  </code></pre>
  
  <p>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.</p>
  
  <p>Also, I am not sure if the "vec2var" step is necessary, so please ignore it if you think it is redundant.</p>
  
  <p>Thanks<br>
  Karl</p>