MCSprocedure: MCSprocedure
In MCS: Model Confidence Set Procedure

Description Usage Arguments Value Author(s) References Examples

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

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

`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 ?

A SSM object

Leopoldo Catania & Mauro Bernardi

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

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

## End(Not run)

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Superior Set Model created	:
             Rank_M        v_M  MCS_M Rank_R        v_R  MCS_R         Loss
sGARCH-norm       4  0.8108405 0.6136      4  1.4124330 0.3696 0.0004042581
sGARCH-std        5  0.9670567 0.5040      5  3.1140463 0.0106 0.0004010655
sGARCH-ged        1 -1.3732854 1.0000      3  0.2171015 0.9940 0.0003986329
sGARCH-snorm      2 -1.3024221 1.0000      2  0.0837766 0.9998 0.0003982803
sGARCH-sstd       3 -0.4687476 1.0000      1 -0.0837766 1.0000 0.0003977886
p-value	:
[1] 0.504

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