Spurious relationships in high dimensional systems with strong or mild persistence

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dc.contributor.author Gonzalo, Jesús
dc.contributor.author Pitarakis, Jean-Yves
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned 2020-12-09T17:19:26Z
dc.date.available 2020-12-09T17:19:26Z
dc.date.issued 2020-12-09
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/31553
dc.description.abstract This paper is concerned with the interactions of persistence and dimensionality in the context of the eigenvalue estimation problem of large covariance matrices arising in cointegration and principal component analysis. Following a review of the early and more recent developments in this area we investigate the behaviour of these eigenvalues in a VAR setting that blends pure unit root, local to unit root and mildly integrated components. Our results highlight the seriousness of spurious relationships that may arise in such Big Data environments even when the degree of persistence of variables involved is mild and is affecting only a small proportion of a large data matrix with important implications for forecasts based on principal component regressions and related methods. We argue that first differencing prior to principal component analysis may be suitable even in stationary ornearly-stationary environments.
dc.language.iso eng
dc.relation.ispartofseries Working paper. Economics
dc.relation.ispartofseries 20-12
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Spurious Cointegration
dc.subject.other Spurious Factors
dc.subject.other Persistence
dc.subject.other High Dimensional Covariances
dc.subject.other Principal Components
dc.title Spurious relationships in high dimensional systems with strong or mild persistence
dc.type workingPaper
dc.identifier.uxxi DT/0000001855
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