Text

Large-Dimensional Factor Analysis Without Moment Constraints

Large-dimensional factor model has drawn much attention in the big-data era, to reduce the dimensionality and extract underlying features using a few latent common factors. Conventional methods for estimating the factor model typically requires finite fourth moment of the data, which ignores the effect of heavy-tailedness and thus may result in unrobust or even inconsistent estimation of the factor space and common components. In this article, we propose to recover the factor space by performing principal component analysis to the spatial Kendall’s tau matrix instead of the sample covariance matrix. In a second step, we estimate the factor scores by the ordinary least square regression. Theoretically, we show that under the elliptical distribution framework the factor loadings and scores as well as the common components can be estimated consistently without any moment constraint. The convergence rates of the estimated factor loadings, scores, and common components are provided. The finite sample performance of the proposed procedure is assessed through thorough simulations. An analysis of a financial dataset of asset returns shows the superiority of the proposed method over the classical principle component analysis method.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.40 No.1 January 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 302-312

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ELLIPTICAL FACTOR MODEL
MULTIVARIATE KENDALL'S TAU MATRIX
ORDINARY LEAST SQUARE REGRESSION

Info Detail Spesifik

https://doi.org/10.1080/07350015.2020.1811101

Pernyataan Tanggungjawab

Yong He, Xinbing Kong, Long Yu, Xinsheng Zhang

Versi lain/terkait

Lampiran Berkas

Komentar