Robust determination for the number of common factors in the approximate factor models

DOI10.1016/J.ECONLET.2016.04.026zbMath1398.62378OpenAlexW2346784074MaRDI QIDQ1668287

Peng Zhang

Publication date: 3 September 2018

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.econlet.2016.04.026

zbMATH Keywords

eigenvaluestransformation functionapproximate factor modeldetermining the number of factorsdominant factorrobust determination

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25)

Related Items (8)

F-test and z-test for high-dimensional regression models with a factor structure ⋮ Robust estimation of the number of factors for the pair-elliptical factor models ⋮ Determining the number of factors in constrained factor models via Bayesian information criterion ⋮ 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 ⋮ Eigenvalue difference test for the number of common factors in the approximate factor models ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

Cites Work

• Factor modeling for high-dimensional time series: inference for the number of factors
• Identifying the finite dimensionality of curve time series
• 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
• Factor profiled sure independence screening
• Modelling multiple time series via common factors
• Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
• Determining the Number of Factors in the General Dynamic Factor Model
• Determining the Number of Factors in Approximate Factor Models

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