The model confidence set package for R. (2018)

• Record Type: Journal Article
• Authors: Bernardi, Mauro; Catania, Leopoldo
• Is Part Of: International journal of computational economics and econometrics. Volume 8:Number 2(2018)
• Page Start: 144
• Page End: 158
• Publication Date: 2018
• Subjects: MCS -- model confidence set -- model choice -- R -- VaR -- value-at-risk; Econometrics -- Periodicals; Economics -- Data processing -- Periodicals; 330.01519505
• Languages: English
• ISSNs: 1757-1170

Abstract

This paper presents the R package MCS which implements the model confidence set (MCS) procedure for model comparison. The MCS procedure consists on a sequence of tests which permits to build 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 test is calculated for an arbitrary loss function, meaning that we could test models on various aspects, such as for example, punctual forecasts and density evaluation. The relevance of the package is shown using an example which aims at illustrating in details the use of the provided functions. The example compares the ability of different models belonging to the generalised autoregressive conditional heteroscedasticity (GARCH) family to predict large financial losses. Codes for reproducibility purposes are also reported.