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Determining the Number of Factors in Approximate Factor Models
Jushan Bai,Serena Ng +1 more
TLDR
In this paper, the authors developed some econometric theory for factor models of large dimensions and proposed some panel C(p) criteria and showed that the number of factors can be consistently estimated using the criteria.Abstract:
In this paper we develop some econometric theory for factor models of large dimensions The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models We propose some panel C(p) criteria and show that the number of factors can be consistently estimated using the criteria The theory is developed under the framework of large cross-sections (N) and large time dimensions (T) No restriction is imposed on the relation between N and T Simulations show that the proposed criteria yield almost precise estimates of the number of factors for configurations of the panel data encountered in practiceread more
Citations
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A Simple Panel Unit Root Test in the Presence of Cross Section Dependence
TL;DR: In this paper, a simple alternative test where the standard unit root regressions are augmented with the cross section averages of lagged levels and first-differences of the individual series is also considered.
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Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
TL;DR: In this article, the authors proposed a new approach to estimation and inference in panel data models with a multifactor error structure where the unobserved common factors are correlated with exogenously given individual-specific regressors, and the factor loadings differ over the cross-section units.
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Forecasting Using Principal Components From a Large Number of Predictors
James H. Stock,Mark W. Watson +1 more
TL;DR: In this paper, the authors consider forecasting a single time series when there are many predictors (N) and time series observations (T), and they show that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large.
Journal ArticleDOI
The Generalized Dynamic-Factor Model: Identification and Estimation
TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).
References
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Book
The econometrics of financial markets
TL;DR: In this paper, Campbell, Lo, and MacKinlay present an attempt by three well-known and well-respected scholars to fill an acknowledged void in the empirical finance literature, a text covering the burgeoning field of empirical finance.
Journal ArticleDOI
The arbitrage theory of capital asset pricing
TL;DR: Ebsco as mentioned in this paper examines the arbitrage model of capital asset pricing as an alternative to the mean variance pricing model introduced by Sharpe, Lintner and Treynor.
Journal ArticleDOI
The Generalized Dynamic-Factor Model: Identification and Estimation
TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).
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Tests for Unit Roots: a Monte Carlo Investigation
TL;DR: In particular, the tests developed by Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984) as mentioned in this paper.