英文标题：
《The Model Confidence Set package for R》
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作者：
Mauro Bernardi and Leopoldo Catania
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最新提交年份：
2014
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英文摘要：
  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).
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中文摘要：
本文介绍了实现Hansen等人（2011）最近开发的模型置信集（MCS）程序的R包MCS。汉森的程序由一系列测试组成，这些测试允许构建一组“高级”模型，在一定的置信水平下，不会拒绝平等预测能力（EPA）的无效假设。EPA统计测试是针对任意损失函数计算的，这意味着我们可以在各个方面测试模型，例如准时预测。该软件包的相关性通过一个旨在详细说明软件包所提供功能的使用的示例来展示。该示例比较了属于ARCH家族的不同模型预测重大财务损失的能力。我们还讨论了使用Ghalanos（2014）开发的流行R包rugarch实现ARCH型模型及其最大似然估计。
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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