Econometric Analysis of Large Factor Models

Jushan Bai and Peng Wang

Annual Review of Economics, 2016, vol. 8, issue 1, 53-80

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.

Keywords: high-dimensional data; factor-augmented regression; FAVAR; number of factors; interactive effects; principal components; regularization; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C01 C33 C38 C55 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (55)

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