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 di?erent 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.
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Paper provided by School of Economics and Management, University of Aarhus in its series Economics Working Papers with number
2005-3.
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
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