Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data

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dc.contributor.author Haldrup, N.
dc.contributor.author Hylleberg, S.
dc.contributor.author Pons, G.
dc.contributor.author Sansó, A.
dc.date.accessioned 2020-04-22T06:50:00Z
dc.identifier.uri http://hdl.handle.net/11201/152085
dc.description.abstract [eng] We propose the multivariate representation of univariate and bivariate (possibly nonstationary) periodic models as a benchmark for the imposition of common periodic correlation (CPC) feature restrictions to obtain parameter parsimony. CPCs are short-run common dynamic features that co-vary across the different days of the week and possibly also across weeks and that can be common across different time series. We also show how periodic models can be used to describe interesting dynamic links in the interaction between stock and flow variables. We apply the proposed modeling framework to a dataset of daily arrivals and departures in airport transit data.
dc.format application/pdf
dc.relation.isformatof https://doi.org/10.1198/073500106000000459
dc.relation.ispartof Journal of Business & Economic Statistics, 2007, vol. 25, num. 1, p. 21-32
dc.rights , 2007
dc.subject.classification 33 - Economia
dc.subject.other 33 - Economics. Economic science
dc.title Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data
dc.type info:eu-repo/semantics/article
dc.date.updated 2020-04-22T06:50:01Z
dc.date.embargoEndDate info:eu-repo/date/embargoEnd/2026-12-31
dc.embargo 2026-12-31
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
dc.identifier.doi https://doi.org/10.1198/073500106000000459


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