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VAR’, ‘VECM’ or ‘TVAR’Forecasting the level of a series estimated by ‘VAR’ / ‘VECM’ or ‘TVAR’
# S3 method for TVAR
predict(object, newdata, n.ahead = 5, newdataTrendStart, ...)# S3 method for VAR
predict(object, newdata, n.ahead = 5, newdataTrendStart, exoPred = NULL, ...)
A matrix of predicted values.
An object of class ‘VAR’, ‘VECM’ or ‘TVAR’
Optional. A new data frame to predict from. This should contain lags of the level of the original series. See Details.
An integer specifying the number of forecast steps.
If ‘newdata’ is provided by the user,
and the estimated model includes a trend,
this argument specifies where the trend should start
Arguments passed to the unexported ‘VAR.gen’ or ‘TVAR.gen’ function
vector/matrix of predictions for the exogeneous variable(s) (with ‘n.ahead’ rows).
Only for ‘VAR’/‘VECM’, not for ‘TVAR’.
Matthieu Stigler
The forecasts are obtained recursively, and are for the levels of the series.
When providing newdata, newdata has to be ordered chronologically, so that the first row/element is the earliest value.
For VECM, the forecasts are obtained by transforming the VECM to a VAR (using function VARrep).
Note that a VECM(lag=p) corresponds to a VAR(lag=p+1), so that if the user provides newdata
for a VECM(lag=p), newdata should actually contain p+1 rows.
lineVar and VECM. VARrep
A more sophisticated predict function, allowing to do sub-sample rolling
predictions: predict_rolling.
data(barry)
barry_in <- head(barry, -5)
barry_out <- tail(barry, 5)
mod_vecm <- VECM(barry_in, lag=2)
mod_var <- lineVar(barry_in, lag=3)
mod_tvar <- TVAR(barry_in, lag=3, nthresh=1, thDelay=1)
pred_vecm <- predict(mod_vecm)
pred_var <- predict(mod_var)
pred_tvar <- predict(mod_tvar)
## compare forecasts on a plot
n <- 30
plot(1:n, tail(barry[,1], n), type="l", xlim=c(0,n))
lines((n-5+1):n, pred_var[,1], lty=2, col=2)
lines((n-5+1):n, pred_vecm[,1], lty=2, col=3)
lines((n-5+1):n, pred_tvar[,1], lty=2, col=4)
legend("bottomright", lty=c(1,2,2,2), col=1:4, legend=c("true", "var", "vecm", "tvar"))
## example for newdata:
all.equal(predict(mod_vecm), predict(mod_vecm, newdata=barry[c(317, 318, 319),]))
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