Abstract

This paper considers the estimation of memory parameters of latent factors in a high-dimensional factor model. We first show that factors can be consistently estimated by principal component analysis (PCA). Then, we treat the estimated factors as observable and apply the local Whittle estimator to find the factor memory. By imposing additional restrictions on bandwidth and sample size, we are able to achieve the standard asymptotic results (consistency and asymptotic normality). Numerical evidence suggests that models with stationary and nonstationary factors have to be treated separately. A model-selection approach is thus proposed for practical applications. Simulations show that our procedure has good finite sample properties. Finally, we provide an empirical application to the Fama-French three-factor model.

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