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Lundi 26 juin 2017

Intervenant : Jianfeng YAO (Hong Kong University)

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

De 15h à 16h15, en salle 08 à l'ENSAE : 3 avenue Pierre Larousse à Malakoff (Tram T3: "Porte de Vanves" ou Métro ligne 13 : "Porte de Vanves" ou "Malakoff Plateau de Vanves")


Identifying the number of factors in a high-dimensional dynamic factor models 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 joint work with Zeng Li and Qinwen Wang

Ce séminaire est organisé par :

Alexandre TSYBAKOV         (Laboratoire de Statistique-CREST)

Cristina BUTUCEA                (Laboratoire de Statistique-CREST)