forecast_roll: Out-of-sample Forecasting based on Rolling Window
In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

forecast_roll

R Documentation

Out-of-sample Forecasting based on Rolling Window

Description

This function conducts rolling window forecasting.

Usage

forecast_roll(object, n_ahead, y_test, num_thread = 1, ...)

## S3 method for class 'bvharcv'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharcv(x)

## S3 method for class 'bvharcv'
knit_print(x, ...)

## S3 method for class 'olsmod'
forecast_roll(object, n_ahead, y_test, num_thread = 1, ...)

## S3 method for class 'normaliw'
forecast_roll(object, n_ahead, y_test, num_thread = 1, use_fit = TRUE, ...)

## S3 method for class 'ldltmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  num_thread = 1,
  level = 0.05,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

## S3 method for class 'svmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  num_thread = 1,
  level = 0.05,
  use_sv = TRUE,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

Arguments

`object`	Model object
`n_ahead`	Step to forecast in rolling window scheme
`y_test`	Test data to be compared. Use `divide_ts()` if you don't have separate evaluation dataset.
`num_thread`	Number of threads
`...`	not used
`x`	Any object
`digits`	digit option to print
`use_fit`	Use `object` result for the first window. By default, `TRUE`.
`level`	Specify alpha of confidence interval level 100(1 - alpha) percentage. By default, .05.
`stable`	Filter only stable coefficient draws in MCMC records.
`sparse`	Apply restriction. By default, `FALSE`.
`med`	If `TRUE`, use median of forecast draws instead of mean (default).
`lpl`	Compute log-predictive likelihood (LPL). By default, `FALSE`.
`verbose`	Print the progress bar in the console. By default, `FALSE`.
`use_sv`	Use SV term