タイトル(掲載誌)Institute of Social and Economic Research Discussion Papers
一般注記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.
連携機関・データベース国立情報学研究所 : 学術機関リポジトリデータベース(IRDB)(機関リポジトリ)