R/predict.TVAR.R

In tsDyn: Nonlinear Time Series Models with Regime Switching

#'@rdname predict.VAR
#' @export
predict.TVAR <- function(object, newdata, n.ahead=5, 
                        newdataTrendStart, ...){
  
  ## extract parameters, coefs
  lag <- object$lag
  k <- object$k
  include <- object$include
  B <- object$coeffmat  
  Thresh <- getTh(object)
  nthresh <- object$model.specific$nthresh

  
  ## setup starting values (data in y), innovations (0)
  original.data <- object$model[,1:k, drop=FALSE]
  starting <-   myTail(original.data,lag)
  innov <- matrix(0, nrow=n.ahead, ncol=k)  
  
  
  if(!missing(newdata)) {
    if(!inherits(newdata, c("data.frame", "matrix","zoo", "ts"))) stop("Arg 'newdata' should be of class data.frame, matrix, zoo or ts")
    if(nrow(newdata)!=lag) stop(paste0("Please provide newdata with nrow=lag=", lag))
    starting <-  newdata 
  }
  
  ## trend
  if(missing(newdataTrendStart)){
    if(include%in%c("trend", "both")){
      trendStart <- object$t+1
    }  else {
      trendStart <- 0
    }
  } else {
    trendStart <- newdataTrendStart
  }
  
  
  ## use VAR sim
  res <- TVAR.gen(B=B, lag=lag, n=n.ahead, trendStart=trendStart,
                 starting=starting, innov=innov,include=include, 
                 Thresh=Thresh, nthresh=nthresh, ...)
  
  ## format results
  colnames(res) <- colnames(original.data)
  end_rows <- nrow(original.data) + n.ahead
  if(hasArg("returnStarting") && isTRUE(list(...)["returnStarting"])) {
    start_rows <- nrow(original.data)+1 - lag
  } else {
    start_rows <- nrow(original.data)+1
  }
  rownames(res) <- start_rows : end_rows
  
  return(res)
}