%0 Journal Article
%9 Article
%W National Agricultural Library
%~ PubAg
%B Journal of econometrics
%T Using principal component analysis to estimate a high dimensional factor model with high-frequency data
%A Aït-Sahalia, Yacine
%A Xiu, Dacheng
%V 2017 v.201 no.2
%K econometric models
%K economic analysis
%K economic theory
%K principal component analysis
%K variance covariance matrix
%K United States
%M 6107897
%X This paper constructs an estimator for the number of common factors in a setting where both the sampling frequency and the number of variables increase. Empirically, we document that the covariance matrix of a large portfolio of US equities is well represented by a low rank common structure with sparse residual matrix. When employed for out-of-sample portfolio allocation, the proposed estimator largely outperforms the sample covariance estimator.
%D 2017
%= 2019-02-08
%G
%8 2017-12
%V v. 201
%N no. 2
%P pp. 384-399
%R 10.1016/j.jeconom.2017.08.015