Identifying the number of factors from singular values of a large sample auto-covariance matrix

Abstract

Identifying the number of factors in a high-dimensional factor model has attracted much attention in recent years and a general solution to the problem is still lacking. A promising ratio estimator based on the singular values of the lagged autocovariance matrix has been recently proposed in the literature and is shown to have a good performance under some specific assumption on the strength of the factors. Inspired by this ratio estimator and as a first main contribution, this paper proposes a complete theory of such sample singular values for both the factor part and the noise part under the large-dimensional scheme where the dimension and the sample size proportionally grow to infinity. In particular, we provide the exact description of the phase transition phenomenon that determines whether a factor is strong enough to be detected with the observed sample singular values. Based on these findings and as a second main contribution of the paper, we propose a new estimator of the number of factors which is strongly consistent for the detection of all significant factors (which are the only theoretically detectable ones). In particular, factors are assumed to have the minimum strength above the phase transition boundary which is of the order of a constant; they are thus not required to grow to infinity together with the dimension (as assumed in most of the existing papers on high-dimensional factor models). Empirical Monte-Carlo study as well as the analysis of stock returns data attest a very good performance of the proposed estimator. In all the tested cases, the new estimator largely outperforms the existing estimator using the same ratios of singular values.

This is a largely revised version of the previous manuscript (v1 & v2)

References in corpus (6)

• Statistical analysis of factor models of high dimension
• Central limit theorems for eigenvalues in a spiked population model
• Factor modeling for high-dimensional time series: Inference for the number of factors
• Moment approach for singular values distribution of a large auto-covariance matrix
• On singular value distribution of large dimensional auto-covariance matrices
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Cited by in corpus (6)

• Modeling High-Dimensional Time Series: A Factor Model with Dynamically Dependent Factors and Diverging Eigenvalues
• Strong limit of the extreme eigenvalues of a symmetrized auto-cross covariance matrix
• On eigenvalue distributions of large auto-covariance matrices
• Order Determination for Spiked Models
• Factor Modelling for Clustering High-dimensional Time Series
• Factor Models for High-Dimensional Functional Time Series