PDF版本：
LARGE DIMENSIONAL FACTOR ANALYSIS

by Jushan Bai (New York University) & Serena Ng (Columbia University)


Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference.
Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy of observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Contents:

• Introduction
• Factor Models
• Principal Components and Related Identities
• Theory: Stationary Data
• Applications
• Panel Regression Models with a factor Structure in the Errors
• Theory: Non-Stationary Data
• How Precise are the Factor Estimates?
• Conclusions
• References

Readership: Graduate students and faculty in economics, econometrics and statistics.