Text

State-Varying Factor Models of Large Dimensions

This article develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We develop an estimator for the latent factors and state-varying loadings under a large cross-section and time dimension. Our estimator combines nonparametric methods with principal component analysis. We derive the rate of convergence and limiting normal distribution for the factors, loadings, and common components. In addition, we develop a statistical test for a change in the factor structure in different states. We apply the estimator to the U.S. Treasury yields and S&P500 stock returns. The systematic factor structure in treasury yields differs in times of booms and recessions as well as in periods of high market volatility. State-varying factors based on the VIX capture significantly more variation and pricing information in individual stocks than constant factor models.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.40 No.3 July 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1315-1333

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FACTOR ANALYSIS
PRINCIPAL COMPONENTS
KERNEL-REGRESSION
LARGE-DIMENSIONAL PANEL DATA
LARGE N AND T
STATE-VARYING

Info Detail Spesifik

https://doi.org/10.1080/07350015.2021.1927744

Pernyataan Tanggungjawab

Markus Pelger, Ruoxuan Xiong

Versi lain/terkait

Lampiran Berkas

Komentar