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摘要翻译：
本文重新研究了线性因子定价模型中的风险premia估计问题。典型地，实证文献中使用的数据具有以下特征：某些定价因素的弱性、误差的强横截面依赖性和（适度）高横截面维度。利用一个渐近框架，当弱因素的风险暴露局部为零时，资产/投资组合的数量随数据的时间跨度而增长，我们证明了传统的两次估计过程给出了不一致的风险Premia估计。我们提出了一种新的基于样本分裂工具变量回归的估计方法。所提出的风险premia估计量对弱的包含因素和存在较强的未考虑的横截面误差依赖性具有鲁棒性。我们推导了该估计量的多资产弱因子渐近分布，说明了如何构造它的标准误差，在模拟中验证了它的性能，并回顾了一些实证研究。
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英文标题：
《Factor models with many assets: strong factors, weak factors, and the
two-pass procedure》
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作者：
Stanislav Anatolyev and Anna Mikusheva
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最新提交年份：
2019
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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 re-examines the problem of estimating risk premia in linear factor pricing models. Typically, the data used in the empirical literature are characterized by weakness of some pricing factors, strong cross-sectional dependence in the errors, and (moderately) high cross-sectional dimensionality. Using an asymptotic framework where the number of assets/portfolios grows with the time span of the data while the risk exposures of weak factors are local-to-zero, we show that the conventional two-pass estimation procedure delivers inconsistent estimates of the risk premia. We propose a new estimation procedure based on sample-splitting instrumental variables regression. The proposed estimator of risk premia is robust to weak included factors and to the presence of strong unaccounted cross-sectional error dependence. We derive the many-asset weak factor asymptotic distribution of the proposed estimator, show how to construct its standard errors, verify its performance in simulations, and revisit some empirical studies.
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PDF链接：
https://arxiv.org/pdf/1807.04094
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