Estimation of Weak Factor Models

Material type: 文書・図像類

Author: Uematsu, Yoshimasaほか

Publisher: The Institute of Social and Economic Research, Osaka University

Publication date: 2020-03

Material Format: Paper

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Note (General)：: April 2019. Revised March 2020.This paper proposes a novel estimation method for the weak factor models, a slightly stronger version of the approximat...

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Paper

Material Type: 文書・図像類
Title: Estimation of Weak Factor Models
Author/Editor: Uematsu, Yoshimasa
Yamagata, Takashi
Author Heading: Uematsu, Yoshimasa
https://nrid.nii.ac.jp/nrid/1000040835279
Yamagata, Takashi
https://nrid.nii.ac.jp/nrid/1000020813231
Publication, Distribution, etc.: The Institute of Social and Economic Research, Osaka University
Publication Date: 2020-03
Publication Date (W3CDTF): 2020-03
Periodical title: Institute of Social and Economic Research Discussion Papers
No. or year of volume/issue: 1053
Volume: 1053
Pages: 1-17
Text Language Code: eng
Subject Heading: Approximate factor models
Weak factors with sparse factor loadings
Non-asymptotic error bound
Factor selection consistency
Target Audience: 一般
Note (General): April 2019. Revised March 2020.
This paper proposes a novel estimation method for the weak factor models, a slightly stronger version of the approximate factor models of Chamberlain and Rothschild (1983), with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of data covariance matrix grows proportionally to N^＜𝝰k＞ with unknown exponents 0 < 𝝰_k 1 for k = 1,..., r. This is much weaker than the typical assumption on the recent factor models, in which all the r largest eigenvalues diverge proportionally to N. We apply the SOFAR method of Uematsu et al. (2019) to estimate the weak factor models and derive the estimation error bound. Importantly, our method yields consistent estimation of 𝝰_k's as well. A finite sample experiment shows that the performance of the new estimator uniformly dominates that of the principal component (PC) estimator. We apply our method to analyze S&P500 firm security returns and find that the first factor is consistently near strong while the others are indeed weak. Another application demonstrates that forecasting bond yields based on our method outperforms that based on the PC.
References (URI): URI
https://www.iser.osaka-u.ac.jp/library/dp/2019/DP1053R.pdf
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 大阪大学 : 大阪大学学術情報庫　桜華