View source: R/backwardCompatibility.R
predict.gmvar | R Documentation |
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.
## S3 method for class '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
)
object |
an object of class 'gmvar' |
... |
additional arguments passed to |
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 ( |
plot_res |
should the results be plotted? |
mix_weights |
|
nt |
a positive integer specifying the number of observations to be plotted
along with the prediction (ignored if |
Returns a class 'gsmvarpred
' object containing, among the specifications,...
Point forecasts
Prediction intervals, as [, , d]
.
Point forecasts for the mixing weights
Individual prediction intervals for mixing weights, as [, , m]
, m=1,..,M.
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 Unpublished working paper, available as arXiv:2109.13648.
@keywords internal
predict.gsmvar
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