PredPdf | R Documentation |
Method returning the predictive density (pdf).
PredPdf(object, ...) ## S3 method for class 'MSGARCH_SPEC' PredPdf( object, x = NULL, par = NULL, data = NULL, log = FALSE, do.its = FALSE, nahead = 1L, do.cumulative = FALSE, ctr = list(), ... ) ## S3 method for class 'MSGARCH_ML_FIT' PredPdf( object, x = NULL, newdata = NULL, log = FALSE, do.its = FALSE, nahead = 1L, do.cumulative = FALSE, ctr = list(), ... ) ## S3 method for class 'MSGARCH_MCMC_FIT' PredPdf( object, x = NULL, newdata = NULL, log = FALSE, do.its = FALSE, nahead = 1L, do.cumulative = FALSE, ctr = list(), ... )
object |
Model specification of class |
... |
Not used. Other arguments to |
x |
Vector (of size n). Used when |
par |
Vector (of size d) or matrix (of size |
data |
Vector (of size T) of observations. |
log |
Logical indicating if the log-density is returned. (Default: |
do.its |
Logical indicating if the in-sample predictive is returned. (Default: |
nahead |
Scalar indicating the number of step-ahead evaluation.
Valid only when |
do.cumulative |
Logical indicating if predictive density is computed on the
cumulative simulations (typically log-returns, as they can be aggregated).
Only available for |
ctr |
A list of control parameters:
|
newdata |
Vector (of size T*) of new observations. (Default |
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 nsim
paths up to
t = T + T* + nahead
. 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* + nahead
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).
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* + nahead
(matrix of
size nahead
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).
# create model specification spec <- CreateSpec() # load data data("SMI", package = "MSGARCH") # fit the model on the data by ML fit <- FitML(spec = spec, data = SMI) # run PredPdf method in-sample pred.its <- PredPdf(object = fit, log = TRUE, do.its = TRUE) # create a mesh x <- seq(-3,3,0.01) # run PredPdf method on mesh at T + 1 pred.x <- PredPdf(object = fit, x = x, log = TRUE, do.its = FALSE)
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