predict,s_semiarma-method: Point and Interval Forecasts for Seasonal Semi-ARMA Models

Description

Obtain point and interval forecasts based on fitted Seasonal Semi-ARMA models.

Usage

# S4 method for s_semiarma
predict(
  object,
  n.ahead = 1,
  intervals = TRUE,
  alpha = c(0.95, 0.99),
  method = c("norm", "boot"),
  bootMethod = c("simple", "advanced"),
  npaths = 5000,
  quant.type = 8,
  expo = FALSE,
  ...
)

Value

A list with the following elements is returned.

pred: the obtained point forecasts.
interv: the obtained forecasting intervals.
obs: the observation series.
ts_name: the name of the observation series object.

Arguments

object: an object of class "s_semiarma".
n.ahead: a numeric vector of length one that represents the forecasting horizon; assume that object is based on observations at time points \(t=1,2,\dots,n\); forecasts are then obtained for time points \(t=n+1,n+2,\dots,n+\code{n.ahead}\); the default is n.ahead = 1.
intervals: a logical vector of length one that indicates whether or not forecasting intervals should be obtained; the default is intervals = TRUE.
alpha: a numeric vector of variable length that indicates the confidence levels at which to obtain forecasting intervals; the default is alpha = c(0.95, 0.99), i.e. confidence levels of 95 and 99 percent.
method: a character vector that indicates the method used to obtain forecasting intervals; available are theoretical intervals based on the assumption of normal innovations ("norm") and intervals through a bootstrap ("boot"); the default is method = "norm".
bootMethod: a character vector that allows the user to select a bootstrap procedure for the forecasting intervals when method = "boot" is selected; the default bootMethod = "simple" simulates future observations by resampling the obtained residuals; the second approach bootMethod = "advanced" also considers the variation in the ARMA coefficient estimates by simulating and reestimating complete ARMA paths upon which forecasts are obtained (see also the B-ARMARoots algorithm in Lu and Wang, 2020); the second approach is often time-consuming.
npaths: the number of paths to simulate, if the forecasting intervals are obtained via a bootstrap.
quant.type: the method to obtain sample quantiles from the simulated forecasting errors; see also the argument type of the function quantile.
expo: a logical vector of length one; indicates whether the forecasting results should be exponentiated at the end; the default is expo = FALSE.
...: only for comparability with the standard predict method.

Author

Dominik Schulz (Research Assistant) (Department of Economics, Paderborn University),
Author and Package Creator

Details

Assume a Seasonal Semi-ARMA model was fitted using s_semiarma. Pass the resulting object to this method, in order to obtain point and interval forecasts.

Examples

Run this code

# \donttest{
xt <- log(EXPENDITURES)
est <- s_semiarma(xt)
predict(est, n.ahead = 10)
# }

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