The Model Confidence Set package for R

TL;DR

The paper introduces the R package MCS which implements Hansen et al.'s Model Confidence Set procedure, allowing users to identify superior models based on various loss functions with an example on financial loss prediction.

Contribution

The paper presents the R package MCS that implements the Model Confidence Set procedure, facilitating model comparison across different loss functions.

Findings

The package enables constructing sets of superior models at specified confidence levels.

It demonstrates the application of the MCS procedure to ARCH models predicting financial losses.

The example illustrates the package's utility in financial risk modeling.

Abstract

This paper presents the R package MCS which implements 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. The relevance of the package is shown using an example which aims at illustrating in details the use of the functions provided by the package. The example compares the ability of different models belonging to the ARCH family to predict large financial losses. We also discuss the implementation of the ARCH--type models and their maximum likelihood estimation using the popular R package rugarch developed by Ghalanos (2014).