Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion

Cites Work

Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
Consistent selection of the number of change-points via sample-splitting
Factor modeling for high-dimensional time series: inference for the number of factors
Detecting big structural breaks in large factor models
Estimating the common break date in large factor models
Identification and estimation of a large factor model with structural instability
Least squares estimation of large dimensional threshold factor models
Testing for structural breaks in dynamic factor models
Determining the number of factors in approximate factor models by twice K-fold cross validation
Simultaneous multiple change-point and factor analysis for high-dimensional time series
Estimation of large dimensional factor models with an unknown number of breaks
On factor models with random missing: EM estimation, inference, and cross validation
Estimating and testing high dimensional factor models with multiple structural changes
Estimation and inference of change points in high-dimensional factor models
Determining the number of breaks in large dimensional factor models with structural changes
Factor-based imputation of missing values and covariances in panel data of large dimensions
Large dimensional latent factor modeling with missing observations and applications to causal inference
Eigenvalue Ratio Test for the Number of Factors
TESTS FOR PARAMETER INSTABILITY IN DYNAMIC FACTOR MODELS
Testing Hypotheses About the Number of Factors in Large Factor Models
Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities
Inferential Theory for Factor Models of Large Dimensions
Determining the Number of Factors in Approximate Factor Models

This page was built for publication: Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion