Model selection with estimated factors and idiosyncratic components
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AI 根据所提供的论文材料归类：摘要以共同因子与特异成分构成预测模型，并应用于长周期汇率预测，直接对应因子模型和宏观预测。
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使用估计因子与特异成分的模型选择
- 研究问题
- 大数据预测模型同时使用估计的共同因子与特异成分时，如何选择两类成分的子集，并处理各自的估计误差以实现模型选择一致性？
- 方法与思路
- 作者构造同时反映因子和特异成分估计误差的信息准则，推导选择一致性的条件，再通过蒙特卡洛与长周期汇率预测应用考察性质。
- 材料陈述的贡献
- 摘要称，新准则在所给条件下具有选择一致性，既有准则可能不一致；标准 BIC 的不一致性不因横截面与时间维度的关系而消除。
- 阅读关注点
- 核对因子与特异成分估计误差如何进入惩罚项、选择一致性所需的维度增长条件，以及蒙特卡洛与汇率预测的比较设计。
- 阅读边界
- 所给摘要中关于 N 的量级条件缺失比较对象，无法还原阈值；具体一致性假设及长周期汇率预测效果也未说明，需核对原文。
原文摘要
Summary This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big dataset. This hybrid model approach has been explored by recent empirical studies to relax the strictness of pure factor‐augmented model approximations, but no formal model selection procedures have been developed. The main difference to previous factor‐augmented model selection procedures is that we must account for estimation error in the idiosyncratic component as well as the factors. Our main contribution is to show the conditions required for selection consistency of a class of information criteria that reflect this additional source of estimation error. We show that existing factor‐augmented model selection criteria are inconsistent in circumstances where N is of larger order than , where N and T are the cross‐section and time series dimensions of the dataset respectively, and that the standard Bayesian information criterion is inconsistent regardless of the relationship between N and T . We therefore propose a new set of information criteria that guarantee selection consistency in the presence of estimated idiosyncratic components. The properties of these new criteria are explored through a Monte Carlo simulation study. The paper concludes with an empirical application to long‐horizon exchange rate forecasting using a recently proposed model with country‐specific idiosyncratic components from a panel of global exchange rates.