MCS-package: Model Confidence Set procedure

In MCS: Model Confidence Set Procedure

Description

Perform the Model Confidence Set procedure of Hansen et.al (2011) for a given set of loss series belonging to several different models that should be compared

Details

Package:	MCS
Type:	Package
Version:	0.1.3
Date:	2014-07-27
License:	GPL-2

The R package MCS aims to implement the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (2011). The Hansen's procedure consists on a sequence of tests which permits to construct a set of 'superior' models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain confidence level. The EPA statistic tests is calculated for an arbitrary loss function, meaning that we could test models on various aspects, for example punctual forecasts.

Author(s)

Leopoldo Catania & Mauro Bernardi

Maintainer: Leopoldo Catania <[e-mail]>

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)

## End(Not run)

Example output

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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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MCS documentation built on May 2, 2019, 7:55 a.m.