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

TitleIdentifying the number of factors from singular values of a large sample auto-covariance matrix
Authors
Issue Date2016
Citation
The Annals of Statistics (Forthcoming) How to Cite?
AbstractIdentifying 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.
Persistent Identifierhttp://hdl.handle.net/10722/231314
ISSN
2015 Impact Factor: 2.78
2015 SCImago Journal Rankings: 6.653

 

DC FieldValueLanguage
dc.contributor.authorLI, Z-
dc.contributor.authorWang, Q-
dc.contributor.authorYao, JJ-
dc.date.accessioned2016-09-20T05:22:15Z-
dc.date.available2016-09-20T05:22:15Z-
dc.date.issued2016-
dc.identifier.citationThe Annals of Statistics (Forthcoming)-
dc.identifier.issn0090-5364-
dc.identifier.urihttp://hdl.handle.net/10722/231314-
dc.description.abstractIdentifying 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.-
dc.languageeng-
dc.relation.ispartofThe Annals of Statistics-
dc.titleIdentifying the number of factors from singular values of a large sample auto-covariance matrix-
dc.typeArticle-
dc.identifier.emailYao, JJ: jeffyao@hku.hk-
dc.identifier.authorityYao, JJ=rp01473-
dc.identifier.hkuros263176-

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