R/predict.varshrinkest.R

In VARshrink: Shrinkage Estimation Methods for Vector Autoregressive Models

#' Predict method for objects of class varshrinkest
#'
#' Forecating a VAR object of class 'varshrinkest'
#' with confidence bands.
#' This is a modification of vars::predict.varest() for the class
#' "varshrinkest".
#'
#' @param object An object of class 'varshrinkest';
#' generated by \code{VARshrink()}
#' @param n.ahead An integer specifying the number of forecast steps.
#' @param ci The forecast confidence interval
#' @param dumvar Matrix for objects of class ‘vec2var’ or ‘varest’,
#' if the dumvar argument in ca.jo() has been used or if the exogen
#' argument in VARshrink() has been used, respectively. The matrix should
#' have the same column dimension as in the call to ca.jo() or to
#' VARshrink() and row dimension equal to n.ahead.
#' @param ... currently not used.
#' @importFrom utils tail
#' @importFrom stats qnorm
#' @export
predict.varshrinkest <- function (object, ..., n.ahead = 10, ci = 0.95,
                                  dumvar = NULL) {
  K <- object$K
  p <- object$p
  obs <- object$obs
  type <- object$type
  data.all <- object$datamat
  ynames <- colnames(object$y)
  n.ahead <- as.integer(n.ahead)
  Z <- object$datamat[, -c(1:K)]
  B <- Bcoef_sh(object)
  if (type == "const") {
    Zdet <- matrix(rep(1, n.ahead), nrow = n.ahead, ncol = 1)
    colnames(Zdet) <- "const"
  }
  else if (type == "trend") {
    trdstart <- nrow(Z) + 1 + p
    Zdet <- matrix(seq(trdstart, length = n.ahead), nrow = n.ahead,
                   ncol = 1)
    colnames(Zdet) <- "trend"
  }
  else if (type == "both") {
    trdstart <- nrow(Z) + 1 + p
    Zdet <- matrix(c(rep(1, n.ahead), seq(trdstart, length = n.ahead)),
                   nrow = n.ahead, ncol = 2)
    colnames(Zdet) <- c("const", "trend")
  }
  else if (type == "none") {
    Zdet <- NULL
  }
  if (!is.null(eval(object$call$season))) {
    season <- eval(object$call$season)
    seas.names <- paste("sd", 1:(season - 1), sep = "")
    cycle <- tail(data.all[, seas.names], season)
    seasonal <- as.matrix(cycle, nrow = season, ncol = season -
                            1)
    if (nrow(seasonal) >= n.ahead) {
      seasonal <- as.matrix(cycle[1:n.ahead, ], nrow = n.ahead,
                            ncol = season - 1)
    }
    else {
      while (nrow(seasonal) < n.ahead) {
        seasonal <- rbind(seasonal, cycle)
      }
      seasonal <- seasonal[1:n.ahead, ]
    }
    rownames(seasonal) <- seq(nrow(data.all) + 1, length = n.ahead)
    if (!is.null(Zdet)) {
      Zdet <- as.matrix(cbind(Zdet, seasonal))
    }
    else {
      Zdet <- as.matrix(seasonal)
    }
  }
  if (!is.null(eval(object$call$exogen))) {
    if (is.null(dumvar)) {
      stop("\nNo matrix for dumvar supplied, but object varest contains exogenous variables.\n")
    }
    if (!all(colnames(dumvar) %in% colnames(data.all))) {
      stop("\nColumn names of dumvar do not coincide with exogen.\n")
    }
    if (!identical(nrow(dumvar), n.ahead)) {
      stop("\nRow number of dumvar is unequal to n.ahead.\n")
    }
    if (!is.null(Zdet)) {
      Zdet <- as.matrix(cbind(Zdet, dumvar))
    }
    else {
      Zdet <- as.matrix(dumvar)
    }
  }
  Zy <- as.matrix(object$datamat[, 1:(K * (p + 1))])
  yse <- matrix(NA, nrow = n.ahead, ncol = K)
  sig.y <- h_fecov(x = object, n.ahead = n.ahead)
  for (i in 1:n.ahead) {
    yse[i, ] <- sqrt(diag(sig.y[, , i]))
  }
  yse <- -1 * qnorm((1 - ci)/2) * yse
  colnames(yse) <- paste(ci, "of", ynames)
  forecast <- matrix(NA, ncol = K, nrow = n.ahead)
  lasty <- c(Zy[nrow(Zy), ])
  for (i in 1:n.ahead) {
    lasty <- lasty[1:(K * p)]
    Z <- c(lasty, Zdet[i, ])
    forecast[i, ] <- B %*% Z
    temp <- forecast[i, ]
    lasty <- c(temp, lasty)
  }
  colnames(forecast) <- paste(ynames, ".fcst", sep = "")
  lower <- forecast - yse
  colnames(lower) <- paste(ynames, ".lower", sep = "")
  upper <- forecast + yse
  colnames(upper) <- paste(ynames, ".upper", sep = "")
  forecasts <- list()
  for (i in 1:K) {
    forecasts[[i]] <- cbind(forecast[, i], lower[, i], upper[,
                                                             i], yse[, i])
    colnames(forecasts[[i]]) <- c("fcst", "lower", "upper",
                                  "CI")
  }
  names(forecasts) <- ynames
  result <- list(fcst = forecasts, endog = object$y, model = object,
                 exo.fcst = dumvar)
  class(result) <- "varprd"
  return(result)
}