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

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  • Haldrup, Niels
  • Hylleberg, Svend
  • Pons, Gabriel
  • Sanso, Andreu

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.
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Suggested Citation

  • Haldrup, Niels & Hylleberg, Svend & Pons, Gabriel & Sanso, Andreu, 2007. "Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 21-32, January.
  • Handle: RePEc:bes:jnlbes:v:25:y:2007:p:21-32
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    Cited by:

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    2. del Barrio Castro, Tomás & Osborn, Denise R., 2008. "Cointegration For Periodically Integrated Processes," Econometric Theory, Cambridge University Press, vol. 24(1), pages 109-142, February.
    3. del Barrio Castro, Tomás, 2021. "Testing for the cointegration rank between Periodically Integrated processes," MPRA Paper 106603, University Library of Munich, Germany, revised 2021.
    4. Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
    5. Hristos Doucouliagos & Martin Paldam, 2009. "The Aid Effectiveness Literature: The Sad Results Of 40 Years Of Research," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 433-461, July.
    6. Marco Centoni & Gianluca Cubadda, 2011. "Modelling comovements of economic time series: a selective survey," Statistica, Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
    7. del Barrio Castro, Tomás, 2021. "Testing for the cointegration rank between Periodically Integrated processes," MPRA Paper 106603, University Library of Munich, Germany, revised 2021.
    8. Ana Bartolomé & Michael McAleer & Vicente Ramos & Javier Rey-Maquieira, 2009. "Modelling Air Passenger Arrivals in the Balearic and Canary Islands, Spain," Tourism Economics, , vol. 15(3), pages 481-500, September.
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    10. Rosselló, Jaume & Sansó, Andreu, 2017. "Yearly, monthly and weekly seasonality of tourism demand: A decomposition analysis," Tourism Management, Elsevier, vol. 60(C), pages 379-389.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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