[R-SIG-Finance] R: Use VAR model to predict response to change in values of certain variables

[Matthieu Stigler]

Same remark as before, please provide reproducible code!!

Ok, sorry, showed only the case for var, not vec2var. Well it is more
complicated as there is no corresponding lm object.
(reason is the var in level you obtain is estimated under constraint
of cointegration, so estimating yourself with usual lm won't lead to
same results).

So what you do is simply extract the coef and mutliply with your
predicted values, something like (using again last value, so can see
it's same as using predict):
data(Canada)
sjf.vecm <- ca.jo(Canada, ecdet = "none", type = "eigen", K = 2,spec =
"longrun")
converted<-   vec2var(sjf.vecm, r = 1)

coefe<-c( converted$A$A1[1,], converted$deterministic[1],converted$A$A2[1,])
predval<-converted$datamat[nrow(converted$datamat),c(1:9)]
coefe%*%predval
predict(converted, n.ahead=1)

Hope this helps

Mat


2009/11/24 Karl Schriek <kschriek at gmail.com>:
> Hi Mat
>
> Yes, this is exactly what I would like to do. Only problem this that I
> generate the model using VECM and and then convert to VAR form using
> vec2var. (i.e. the object is of type vec2var, not varest)
>
> I am not sure how to extract the lm from vec2var.
>
>
> Karl
>
>
> On Mon, Nov 23, 2009 at 3:46 PM, Matthieu Stigler <
> matthieu.stigler at gmail.com> wrote:
>
>> Hi Karl
>>
>> Please provide reproducible code when you ask questions, we don't have
>> access to your local drive!!
>> So use rather datasets in R, as for example:
>> data(Canada)
>> colnames(Canada)
>>  var.2c <- VAR(Canada, p = 1, type = "none")
>>
>> You will note that there is a predict.varest function which does forecasts:
>>     pred<-predict(var.2c, n.ahead = 8, ci = 0.95)
>>
>> This is not exactly what you want... you want to change some
>> forecasted values, and see the influence, right? This is not available
>> i the predict.varest. You will need to do some manipulations,
>> basically extracting the lm object and using predict(lm, newdata)
>>
>>
>> Construct data frame with forecasted values. It should include all
>> variables you see when in the output of var.2c. To keep simple, I used
>> the simplest model (1 lag, no intercept), so need only to give 4
>> values (the lag-1). For the example, I took last dat of data Canada,
>> change as you want:
>> :
>> yourPred<-as.data.frame(matrix(Canada[nrow(Canada),], nrow=1))
>> colnames(yourPred)<-colnames(model.frame(var.2c$varresult$e))[-1] #you
>> need to give same names as in var result!
>>
>> predict(var.2c$varresult$e, newdata=yourPred)
>>
>> Note that this is exactly the same as what does forecast.varest,
>> except you can now change it manually!
>>
>> pred$fcst$e[1,1]
>>
>> And now you can change the values in yourPred as you want.
>>
>> Hope this helps
>>
>> Mat
>>
>> 2009/11/23 Karl Schriek <kschriek at gmail.com>:
>> > 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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>> >
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