predict.gsmvar {gmvarkit} | R Documentation |
Predict method for class 'gsmvar' objects
Description
predict.gsmvar
is a predict method for class 'gsmvar'
objects. The forecasts of
the GMVAR, StMVAR, and G-StMVAR models 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 class 'gsmvar'
predict(
object,
...,
n_ahead,
nsim = 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
)
Arguments
object |
an object of class |
... |
additional arguments passed to |
n_ahead |
how many steps ahead should be predicted? |
nsim |
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 |
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.
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 Unpublished working paper, available as arXiv:2109.13648.
@keywords internal
See Also
Examples
# GMVAR(2, 2), d=2 model
params22 <- c(0.36, 0.121, 0.223, 0.059, -0.151, 0.395, 0.406, -0.005,
0.083, 0.299, 0.215, 0.002, 0.03, 0.484, 0.072, 0.218, 0.02, -0.119,
0.722, 0.093, 0.032, 0.044, 0.191, 1.101, -0.004, 0.105, 0.58)
mod22 <- GSMVAR(gdpdef, p=2, M=2, d=2, params=params22)
p1 <- predict(mod22, n_ahead=10, pred_type="median", nsim=500)
p1
p2 <- predict(mod22, n_ahead=10, nt=20, lty=1, nsim=500)
p2
p3 <- predict(mod22, n_ahead=10, pi=c(0.99, 0.90, 0.80, 0.70),
nt=30, lty=0, nsim=500)
p3
# StMVAR(2, 2), d=2 model
params22t <- c(0.36, 0.121, 0.223, 0.059, -0.151, 0.395, 0.406, -0.005,
0.083, 0.299, 0.215, 0.002, 0.03, 0.484, 0.072, 0.218, 0.02, -0.119,
0.722, 0.093, 0.032, 0.044, 0.191, 1.101, -0.004, 0.105, 0.58, 3, 4)
mod22t <- GSMVAR(gdpdef, p=2, M=2, d=2, params=params22t, model="StMVAR")
p1 <- predict(mod22t, n_ahead=12, pred_type="median", nsim=500, pi=0.9)
p1