MSGARCH (version 1.3)

Pred: Predictive density.

Description

Method returning the predictive probability density.

Usage

Pred(object, ...)
# S3 method for MSGARCH_SPEC
Pred(object, x = NULL, par = NULL, data = NULL,
  log = FALSE, do.its = FALSE, n.ahead = 1L, ctr = list(), ...)
# S3 method for MSGARCH_ML_FIT
Pred(object, x = NULL, new.data = NULL,
  log = FALSE, do.its = FALSE, n.ahead = 1L, ctr = list(), ...)
# S3 method for MSGARCH_MCMC_FIT
Pred(object, x = NULL, new.data = NULL,
  log = FALSE, do.its = FALSE, n.ahead = 1L, ctr = list(), ...)

Arguments

object

Model specification of class MSGARCH_SPEC created with CreateSpec or fit object of type MSGARCH_ML_FIT created with FitML or MSGARCH_MCMC_FIT created with FitMCMC.

...

Not used. Other arguments to Pred.

x

Vector (of size n). Used when do.its = FALSE.

par

Vector (of size d) or matrix (of size n.mcmc x d) of parameter estimates where d must have the same length as the default parameters of the specification.

data

Vector (of size T) of observations.

log

Logical indicating if the log-density is returned. (Default: log = FALSE)

do.its

Logical indicating if the in-sample predictive is returned. (Default: do.its = FALSE)

n.ahead

Scalar indicating the number of step-ahead evaluation. Valid only when do.its = FALSE. (Default: n.ahead = 1L)

ctr

A list of control parameters:

• n.sim (integer >= 0) : Number indicating the number of simulation done for the evaluation of the density at n.ahead > 1. (Default: n.sim = 10000L)

new.data

Vector (of size T*) of new observations. (Default new.data = NULL)

Value

A vector or matrix of class MSGARCH_PRED. If do.its = FALSE: (Log-)predictive of the points x at t = T + T* + 1, ... ,t = T + T* + n.ahead (matrix of size n.ahead x n). If do.its = TRUE: In-sample predictive of data if x = NULL (vector of size T + T*) or in-sample predictive of x (matrix of size (T + T*) x n).

Details

If a matrix of MCMC posterior draws is given, the Bayesian predictive probability density is calculated. Two or more step-ahead predictive probability density are estimated via simulation of n.sim paths up to t = T + T* + n.ahead. The predictive distribution are then inferred from these simulations via a Gaussian Kernel density. If do.its = FALSE, the vector x are evaluated as t = T + T* + 1, ... ,t = T + T* + n.ahead realization. If do.its = TRUE and x is evaluated at each time t up top time t = T + T*. Finally, if x = NULL the vector data is evaluated for sample evaluation of the predictive denisty ((log-)likelihood of each sample points).

Examples

# NOT RUN {
# load data
data("SMI", package = "MSGARCH")

# create model specification
# MS(2)-GARCH(1,1)-Normal (default)
spec <- CreateSpec()

# fit the model on the data by ML
fit <- FitML(spec = spec, data = SMI)

# run Pred method in-sample
pred.its <- Pred(object = fit, log = TRUE, do.its = TRUE)

# create a mesh
x <- seq(-3,3,0.01)

# run pred method on mesh at T + 1
pred.x <- Pred(object = fit, x = x, log = TRUE, do.its = FALSE)
# }