Discovering general and sectorial trends in a large set of time series

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dc.contributor.author Carlomagno Real, Guillermo
dc.contributor.author Espasa, Antoni
dc.contributor.other Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2020-09-29T17:42:04Z
dc.date.available 2020-09-29T17:42:04Z
dc.date.issued 2020-09-29
dc.identifier.issn 2387-0303
dc.identifier.uri http://hdl.handle.net/10016/30899
dc.description.abstract The objective of this research note is to extend the pairwise procedure studied by Car- lomagno and Espasa (ming) to the case of general and sectorial trends. The extension allows to discover subsets of series that share general and/or sectorial stochastic trends between a (possible large) set of time series. This could be useful to model and forecast all of the series under analysis. Our approach does not need to assume pervasiveness of the trends, nor impose special restrictions on the serial or cross-sectional idiosyncratic correlation of the series. Additionally, the asymptotic theory works both, with finite N and T ! 1, and with [T;N] ! 1. In a Monte Carlo experiment we show that the extended procedure can produce reliable results in finite samples.
dc.language.iso eng
dc.relation.ispartofseries Working paper. Statistics and Econometrics
dc.relation.ispartofseries 20-07
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Cointegration
dc.subject.other Factor Models
dc.subject.other Disaggregation
dc.subject.other Pairwise Tests
dc.subject.other Heteroskedasticity
dc.title Discovering general and sectorial trends in a large set of time series
dc.type workingPaper
dc.subject.jel C01
dc.subject.jel C22
dc.subject.jel C32
dc.subject.jel C53
dc.identifier.uxxi DT/0000001812
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