Spanning latent and observable factors

IF 9.9 3区 经济学 Q1 ECONOMICS
E. Andreou , P. Gagliardini , E. Ghysels , M. Rubin
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引用次数: 0

Abstract

Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Many applications in finance and macroeconomics, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a test to answer a question which naturally comes up in discussions regarding latent versus observable factors: do latent and observable factors span the same space? We derive asymptotic properties of a formal test and propose a bootstrap version with improved small sample properties. We find empirical evidence for a small number of factors common between a small number of traditional Fama–French risk factors – or returns on a few stocks (i.e. “magnificent” 5 or 7) – and large panels of US, North American and international portfolio returns.
跨越潜在因素和可观测因素
因子分析是一种广泛使用的工具,通过一组小维度的潜在因素来总结高维面板数据。在金融和宏观经济学中的许多应用,通常集中在具有经济解释的可观察因素上。本文的目的是提供一个测试来回答在讨论潜在因素和可观察因素时自然会出现的问题:潜在因素和可观察因素是否跨越同一空间?我们推导了一个形式检验的渐近性质,并提出了一个改进小样本性质的自举版本。我们发现,在少数传统法玛-法伦风险因素(或少数股票的回报率(即“宏伟的”5或7))与美国、北美和国际投资组合回报率的大面板之间,存在少数共同因素的经验证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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