Transformed contribution ratio test for the number of factors in static approximate factor models

DOI10.1016/j.csda.2017.03.005zbMath1464.62187OpenAlexW2601801618MaRDI QIDQ1654280

Y. Aharonov

Publication date: 7 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2017.03.005

zbMATH Keywords

eigenvaluesnumber of factorsapproximate factor modeltransformed contribution ratio

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Hypothesis testing in multivariate analysis (62H15)

Related Items (4)

Robust estimation of the number of factors for the pair-elliptical factor models ⋮ On determination of the number of factors in an approximate factor model ⋮ Detecting irrelevant variables in possible proxies for the latent factors in macroeconomics and finance ⋮ Robust factor number specification for large-dimensional elliptical factor model

Cites Work

• Factor modeling for high-dimensional time series: inference for the number of factors
• Improved penalization for determining the number of factors in approximate factor models
• Winding number criterion for existence and uniqueness of equilibrium in linear rational expectations models
• Eigenvalue Ratio Test for the Number of Factors
• Consistently determining the number of factors in multivariate volatility modelling
• Modelling multiple time series via common factors
• Testing Hypotheses About the Number of Factors in Large Factor Models
• Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
• Forecasting Using Principal Components From a Large Number of Predictors
• Determining the Number of Factors in the General Dynamic Factor Model
• Determining the Number of Factors in Approximate Factor Models
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements

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