Year: 2016
Type: review
Abstract: Large factor models use a few latent factors to characterize the co-movement of economic variables in a high-dimensional data set. High dimensionality brings challenges as well as new insights into the advancement of econometric theory. Because of their ability to effectively summarize information in large data sets, factor models have been increasingly used in economics and finance. The factors, estimated from the high-dimensional data, can, for example, help improve forecasting, provide efficient instruments, control for nonlinear unobserved heterogeneity, and capture cross-sectional dependence. This article reviews the theory on estimation and statistical inference of large factor models. It also discusses important applications and highlights future directions.
Source: Annual Review of Economics
Authors: Jushan Bai, Peng Wang
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W1953394243