MCS (version 0.1.3)

MCSprocedure: MCSprocedure

Description

Perform the Model Confidence Set procedure of Hansen et.al. (2011)

Usage

MCSprocedure(Loss, alpha = 0.15, B = 5000, cl = NULL,
                         ram.allocation = TRUE, statistic = "Tmax", k = NULL, min.k = 3,
                         verbose = TRUE)

Arguments

Loss

A matrix or something coercible to that (as.matrix) which contains the loss series per each competing model

alpha

a scalar in (0,1) indicating the confidence level of the tests

B

an integer indicating the number of bootstrapped samples used to construct the statistic test

cl

A cl object created by calling makecl from the parallel package. If it is not NULL, then this will be used for parallel processing (remember to stop the cl on completion)

ram.allocation

Default TRUE, only considered if cl in not NULL. Let the function decide how to allocate memory when cl are supplied ? Usefull when many models are available.

statistic

Possible choice are : Tmax and TR. See Hansen et.al. (2011) [pag. 465] and Bernardi M. and Catania L. (2014) for more information.

k

The number of block bootstrap length. If NULL (default) the block length is determined by the max number of significants parameters resulted after fitting an AR(p) process on all the Loss differences as suggested by Hansen et.al. (2011)

min.k

If k=NULL the minimum length of the the blocks, by default equal to 3

verbose

Information abount the MCS procedure should be printed ?

Value

A SSM object

References

Hansen PR, Lunde A, Nason JM (2011). The model confidence set. Econometrica, 79(2), 453-497.

Bernardi M. and Catania L. (2014) The Model Confidence Set package for R. URL http://arxiv.org/abs/1410.8504

Examples

# NOT RUN {
library(MCS)
data(Loss)
MCS <- MCSprocedure(Loss=Loss[,1:5],alpha=0.2,B=5000,statistic='Tmax',cl=NULL)
# }