 Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport DataNiels Haldrup (), Svend Hylleberg, Gabriel Pons Rotger, Jaume Rossello () and Andreu Sansó Economics Working Papers from Department of Economics and Business Economics, Aarhus University Abstract: This paper presents a new framework for coping with problems often encountered when modeling seasonal high frequency data containing both flow and stock variables. The idea is to apply a multivariate weekly representation of a daily periodic model and to exploit the possible cointegration and common feature properties of the variables in order to obtain a more parsimonious model representation. We introduce the notion of common periodic correlations, which are common features that co-vary - possibly with a phase shift - across the different days of the week and possibly also across weeks. The paper also suggests a way of modelling the dynamic interaction of stock and flow variables within a periodic setting that is similar to the concept of multicointegration among integrated variables. The proposed modelling framework is applied to a data set of daily arrivals and departures in the airport of Mallorca. Keywords: Periodic autoregression; seasonality; high frequency data; cointegration; multicointegration; common features (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aah:aarhec:2005-03 Access Statistics for this paper More papers in Economics Working Papers from Department of Economics and Business Economics, Aarhus University |
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