Long Memory Factor Model: On Estimation of Factor Memories
研究了近似因子模型中潜在因子整合阶数的估计问题，提出两阶段估计量并证明其一致性和渐近正态性，应用于美国金融机构对数平方收益率发现因子存在长记忆性且2007年后更持久。
This article considers the estimation of the integration orders of the latent factors in an approximate factor model. Both the common factors and idiosyncratic error terms are potentially nonstationary fractionally integrated processes. We propose a two-stage approach to estimate the factor memories. We show the consistency and asymptotic normality of the proposed estimator. Applying the estimator to the log-squared returns of the U.S. financial institutions, we find evidence of long memory in the estimated factor. We also find that the factor becomes more persistent after 2007.