GAS (version 0.3.4.1)

MultiGASFor: Forecast with multivariate GAS models

Description

Forecast with multivariate GAS models. One-step ahead prediction of the conditional density is available in closed form. Multistep ahead prediction are performed by simulation as detailed in Blasques et al. (2016).

Usage

MultiGASFor(mGASFit, H = NULL, Roll = FALSE, out = NULL, B = 10000,
                     Bands = c(0.1, 0.15, 0.85, 0.9), ReturnDraws = FALSE)

Value

An object of the class mGASFor

Arguments

mGASFit

An object of the class mGASFit created using the function MultiGASFit

H

numeric Forecast horizon. Ignored if Roll = TRUE

Roll

logical Forecast should be made using a rolling procedure ? Note that if Roll = TRUE, then out has to be specified.

out

matrix of out of sample observation of dimension H x N for rolling forecast. N refers to the cross sectional dimension.

B

numeric Number of draws from the iH-step ahead distribution if Roll = FALSE.

Bands

numeric Vector of probabilities representing the confidence band levels for multistep ahead parameters forecasts. Only if Roll = FALSE.

ReturnDraws

logical Return the draws from the multistep ahead predictive distribution when Roll = FALSE ?

Author

Leopoldo Catania

References

Blasques F, Koopman SJ, Lasak K, and Lucas, A (2016). "In-sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation-Driven Models." International Journal of Forecasting, 32(3), 875-887. tools:::Rd_expr_doi("10.1016/j.ijforecast.2016.04.002").

Examples

if (FALSE) {
# Specify a GAS model with multivatiate  Student-t conditional
# distribution and time-varying scales and correlations.

# Stock returns forecast

set.seed(123)

data("StockIndices")

mY = StockIndices[, 1:2]

# Specification mvt
GASSpec = MultiGASSpec(Dist = "mvt", ScalingType = "Identity",
                       GASPar = list(location = FALSE, scale = TRUE,
                                     correlation = TRUE, shape = FALSE))

# Perform H-step ahead forecast with confidence bands

# Estimation
Fit = MultiGASFit(GASSpec, mY)

# Forecast
Forecast  = MultiGASFor(Fit, H = 50)

Forecast

# Perform 1-Step ahead rolling forecast

InSampleData  = mY[1:1000, ]
OutSampleData = mY[1001:2404, ]

# Estimation
Fit = MultiGASFit(GASSpec, InSampleData)

Forecast  = MultiGASFor(Fit, Roll = TRUE, out = OutSampleData)

Forecast
}