GARCH.X (version 1.0)

predict: Predict GARCHX future time series values

Description

Predicts values for GARCHX model

Usage

predict(model, X, n_pred)

Value

Vector of predicted time series data

Arguments

model

GARCHX object

X

Exogenous covariates for predictions

n_pred

Number of predictions into the future

References

Francq, C. and Thieu, L.Q.(2018). QML Inference for Volatility Models with Covariates. Econometric Theory, Cambridge University Press

Examples

set.seed(123)
pi <- c(1, 0, 0, 4)
n <- 2000
d <- length(pi)
valinit <- 100
n2 <- n + d + 1
omega <- 0.1
alpha <- 0.2
beta <- 0.3
delta <- 2
e<-rnorm(n2+valinit)
Y<-e
for (t in 2:n2)
 Y[t]<- 0.2*Y[t-1]+e[t]
x<-exp(Y)
X <- matrix(0, nrow = (n+valinit), ncol = length(pi))
for(j in 1:d)
 X[, j] <- x[(d+2-j):(n+d+1-j+valinit)]
data <- GARCH.X::simulate(n, omega, alpha, beta, delta, X, pi, valinit = valinit)
model <- GARCHX_select(eps = data$eps, X = data$X)
n_pred = 10
test.X <- data$X[(n-n_pred+1):n, ]
predictions <- predict(model = model, X = test.X, n_pred = n_pred)