Selecting the number of factors in multi-variate time series

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20) Inference from stochastic processes (62Mxx)

Cites Work

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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
Robust covariance estimation for approximate factor models
Robust high-dimensional factor models with applications to statistical machine learning
A robust procedure to build dynamic factor models with cluster structure
Dynamic factor models with infinite-dimensional factor spaces: one-sided representations
Principal components estimation and identification of static factors
Nonstationary dynamic factor analysis
Eigenvalue Ratio Test for the Number of Factors
Estimation of latent factors for high-dimensional time series
Determining the Number of Factors and Lag Order in Dynamic Factor Models: A Minimum Entropy Approach
Testing Hypotheses About the Number of Factors in Large Factor Models
Funds, Factors, and Diversification in Arbitrage Pricing Models
Identifying a Simplifying Structure in Time Series
Testing for Common Trends
A Powerful Portmanteau Test of Lack of Fit for Time Series
Forecasting Using Principal Components From a Large Number of Predictors
A Randomized Sequential Procedure to Determine the Number of Factors
On The Peña–Box Model
On a new procedure for identifying a dynamic common factor model
Determining the Number of Factors in the General Dynamic Factor Model
A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets
Inferential Theory for Factor Models of Large Dimensions
Determining the Number of Factors in Approximate Factor Models
Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
The Generalized Dynamic Factor Model
Modeling High-Dimensional Time Series: A Factor Model With Dynamically Dependent Factors and Diverging Eigenvalues
Estimating Number of Factors by Adjusted Eigenvalues Thresholding
Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data
Large-Dimensional Factor Analysis Without Moment Constraints

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