Large Dimensional Factor Analysis

By Jushan Bai, Serena Ng. 2008

426 citations recorded by the source corpus.

Econometric analysis of large dimensional factor models has been a heavily researched topic in recent years. This review surveys the main theoretical results that relate to static factor models or dynamic factor models that can be cast in a static framework. Among the topics covered are 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. The fundamental result that justifies these analyses is that the method of asymptotic principal components consistently estimates the true factor space. We use simulations to better understand the conditions that can affect the precision of the factor estimates.

Subfields: Economics and Econometrics, Finance

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Principal Components and Regularized Estimation of Factor Models (2017, methodology): “ with normalization Λ′ N = Ir. 3The method of APC is due to Connor and Korajzcyk (1986). Forni et al. (2000) and Stock and Watson (2002a,b) initiated interests in large dimensional factor models. See Bai and Ng (2008) for a review of this work. Fan et al. (2013) shows consistency of the factor estim”
The Impact of Machine Learning on Economics (2018, methodology): “Another natural example is credit scoring; an economics paper by Bjorkegren and Grissen (2017) uses ML methods to predict loan repayment using mobile phone data.”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, methodology): “Bai and Ng (2008) provide a technical survey of the econometric theory for principal components and related DFM methods.”
Cross-section dependence in nonstationary panel models: a novel estimator (2009, background): “The only difference to our Z = β̂′X example is that both (latent) elements of µ̂•t (factors, loadings) are treated as parameters (Bai & Ng, 2008).”
State-Varying Factor Models of Large Dimensions (2018, neutral): “Breitung and Eickmeier (2011), Han and Inoue (2015) and Bai et al.”
Bi-cross-validation for factor analysis (2015, background): “In psychology, the factor model dates back at least to the paper of Spearman in 1904 [35].”
Large-dimensional factor modeling based on high-frequency observations (2019, neutral): “Then the following consistency results hold: 18For a more detailed discussion see Bai (2003) and Bai and Ng (2008).”
Consistent Factor Estimation in Dynamic Factor Models with Structural Instability (2013, background): “For recent contributions and discussions of this large literature see Bai and Ng (2008a), Eickmeier and Ziegler (2008), Chudik and Pesaran (2011) and Stock and Watson (2011). break in many factor loadings around the beginning of the Great Moderation in 1984.”
Variable Selection in Predictive Regressions (2013, background): “The relation between static and dynamic factors can be found in Forni, Hallin, Lippi, and Reichlin (2005), Bai and Ng (2008b), Stock and Watson (2005).”
Oracle Inequalities for High Dimensional Vector Autoregressions (2012, methodology): “For surveys on factor analysis in the context of time series see Stock and Watson (2006), Bai and Ng (2008) and Stock and Watson (2011).”
Cross-Sectional Dependence in Panel Data Analysis (2012, background): “The main theoretical results for large dimensional factor analysis using principal components are reviewed in an excellent survey by Bai and Ng (2008).”
Asymptotics of the principal components estimator of large factor models with weakly influential factors (2012, methodology): “Formally, we will consider a sequence of models (1) indexed by the cross-sectional dimension n; so that L(n)0L(n) D ! 0 (4)”
DYNAMIC FACTOR MODELS (2018, background): “…survey is to describe, at a level that is specific enough to be useful to researchers new to the area, the key theoretical results, applications, and empirical findings in the recent literature on DFMs. Bai and Ng (2008) and Stock and Watson (2006) provide complementary surveys of this literature.”