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摘要翻译：
我们研究由观察到的对未知因素有解释力的协变量增强的因素模型。在金融因素模型中，未知因素可以用一些可观察到的代理来合理地解释，如Fama-French因素。在扩散指数预测中，确定的因素与几个直接可测量的经济变量如消费-财富变量、财务比率和期限利差密切相关。利用这些协变量，即使仅在有限维的旋转矩阵上，因子和载荷都是可识别的。为了结合这些协变量的解释能力，我们提出了一种平滑的主成分分析(PCA)：(i)将数据回归到观察到的协变量上，(ii)取拟合数据的主成分来估计负荷和因子。这使我们能够准确地估计因素中已解释和未解释成分的百分比，从而评估协变量的解释能力。我们表明，与基准方法相比，估计因子和负载都可以以改进的收敛速度估计。改善的程度取决于信号的强度，代表了协变量对因素的解释能力。所提出的估计量对可能的重尾分布具有鲁棒性。运用该模型对美国国债风险进行预测，发现所观察到的宏观经济特征对各因素具有较强的解释力。与直接用于预测相比，将这些特征用于估计公因子时，预测的增益更大。
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英文标题：
《Augmented Factor Models with Applications to Validating Market Risk
Factors and Forecasting Bond Risk Premia》
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作者：
Jianqing Fan, Yuan Ke, Yuan Liao
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最新提交年份：
2018
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama-French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the factors and loadings are identifiable up to a rotation matrix even only with a finite dimension. To incorporate the explanatory power of these covariates, we propose a smoothed principal component analysis (PCA): (i) regress the data onto the observed covariates, and (ii) take the principal components of the fitted data to estimate the loadings and factors. This allows us to accurately estimate the percentage of both explained and unexplained components in factors and thus to assess the explanatory power of covariates. We show that both the estimated factors and loadings can be estimated with improved rates of convergence compared to the benchmark method. The degree of improvement depends on the strength of the signals, representing the explanatory power of the covariates on the factors. The proposed estimator is robust to possibly heavy-tailed distributions. We apply the model to forecast US bond risk premia, and find that the observed macroeconomic characteristics contain strong explanatory powers of the factors. The gain of forecast is more substantial when the characteristics are incorporated to estimate the common factors than directly used for forecasts.
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PDF链接：
https://arxiv.org/pdf/1603.07041
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