Estimation of large dimensional factor models with an unknown number of breaks

DOI10.1016/j.jeconom.2018.06.019zbMath1452.62414OpenAlexW2300519491MaRDI QIDQ1792477

Shujie Ma, Liangjun Su

Publication date: 12 October 2018

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://ink.library.smu.edu.sg/soe_research/1789

zbMATH Keywords

information criterionstructural changegroup Lassofactor modelbreak pointsuper-consistencytime-varying parameterprincipal componentfused Lasso

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (16)

Estimation of high dimensional factor model with multiple threshold-type regime shifts ⋮ Determining the number of breaks in large dimensional factor models with structural changes ⋮ Simultaneous multiple change-point and factor analysis for high-dimensional time series ⋮ Shrinkage estimation of multiple threshold factor models ⋮ Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion ⋮ Group fused Lasso for large factor models with multiple structural breaks ⋮ Quasi-maximum likelihood estimation of break point in high-dimensional factor models ⋮ Detection of Multiple Structural Breaks in Large Covariance Matrices ⋮ The likelihood ratio test for structural changes in factor models ⋮ Estimating and testing high dimensional factor models with multiple structural changes ⋮ Estimation and inference of change points in high-dimensional factor models ⋮ Nonparametric estimation of large covariance matrices with conditional sparsity ⋮ Estimating change-point latent factor models for high-dimensional time series ⋮ TESTING FOR STRUCTURAL CHANGES IN FACTOR MODELS VIA A NONPARAMETRIC REGRESSION ⋮ Testing for time-varying factor loadings in high-dimensional factor models ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

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