predict.gmvar: DEPRECATED! USE THE FUNCTION predict.gsmvar INSTEAD! Predict method for class 'gmvar' objects

Description

predict.gsmvar is a predict method for class 'gsmvar' objects. The forecasts of the GMVAR model are computed by performing independent simulations and using the sample medians or means as point forecasts and empirical quantiles as prediction intervals. For one-step-ahead predictions using the exact conditional mean is also supported.

Usage

# S3 method for gmvar
predict(
  object,
  ...,
  n_ahead,
  n_simu = 2000,
  pi = c(0.95, 0.8),
  pi_type = c("two-sided", "upper", "lower", "none"),
  pred_type = c("median", "mean", "cond_mean"),
  plot_res = TRUE,
  mix_weights = TRUE,
  nt
)

Value

Returns a class 'gsmvarpred' object containing, among the specifications,...

$pred: Point forecasts
$pred_int: Prediction intervals, as [, , d].
$mix_pred: Point forecasts for the mixing weights
mix_pred_int: Individual prediction intervals for mixing weights, as [, , m], m=1,..,M.

Arguments

object: an object of class 'gmvar'
...: additional arguments passed to grid (ignored if plot_res==FALSE) which plots grid to the figure.
n_ahead: how many steps ahead should be predicted?
n_simu: to how many independent simulations should the forecast be based on?
pi: a numeric vector specifying the confidence levels of the prediction intervals.
pi_type: should the prediction intervals be "two-sided", "upper", or "lower"?
pred_type: should the prediction be based on sample "median" or "mean"? Or should it be one-step-ahead forecast based on the exact conditional mean ("cond_mean")? Prediction intervals won't be calculated if the exact conditional mean is used.
plot_res: should the results be plotted?
mix_weights: TRUE if forecasts for mixing weights should be plotted, FALSE in not.
nt: a positive integer specifying the number of observations to be plotted along with the prediction (ignored if plot_res==FALSE). Default is round(nrow(data)*0.15).

References

Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.
Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.

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