摘要翻译:
本文发展了一个大维状态变因子模型的推理理论。与常因子模型不同,负载是某些循环状态过程的一般函数。我们提出了一个在大截面和大时间维下的潜在因素和状态变化载荷的估计量。我们的估计器结合了非参数方法和主成分分析。导出了因子、载荷和公共分量的收敛速度和极限正态分布。此外,我们对不同状态下因子结构的变化进行了统计检验。我们将该估计值应用于美国国债收益率和标准普尔500指数股票收益率。国债收益率的系统因素结构在繁荣和衰退时期以及在市场高度波动的时期都是不同的。基于VIX的状态变化因子比不变因子模型捕捉到了更多的个股变化和定价信息。
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英文标题:
《State-Varying Factor Models of Large Dimensions》
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作者:
Markus Pelger, Ruoxuan Xiong
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最新提交年份:
2020
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分类信息:
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:
This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We develop an estimator for the latent factors and state-varying loadings under a large cross-section and time dimension. Our estimator combines nonparametric methods with principal component analysis. We derive the rate of convergence and limiting normal distribution for the factors, loadings and common components. In addition, we develop a statistical test for a change in the factor structure in different states. We apply the estimator to U.S. Treasury yields and S&P500 stock returns. The systematic factor structure in treasury yields differs in times of booms and recessions as well as in periods of high market volatility. State-varying factors based on the VIX capture significantly more variation and pricing information in individual stocks than constant factor models.
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PDF链接:
https://arxiv.org/pdf/1807.02248