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

Author

Listed:
  • Niels Haldrup
  • Svend Hylleberg
  • Gabriel Pons
  • Jaume Rosselló
  • Andreu Sansó

    (Department of Economics, University of Aarhus, Denmark)

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.

Suggested Citation

  • Niels Haldrup & Svend Hylleberg & Gabriel Pons & Jaume Rosselló & Andreu Sansó, 2005. "Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data," Economics Working Papers 2005-03, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:aarhec:2005-03
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. del Barrio Castro, Tomás, 2021. "Testing for the cointegration rank between Periodically Integrated processes," MPRA Paper 106603, University Library of Munich, Germany, revised 2021.
    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. 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.
    4. 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.
    5. 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.
    6. Cubadda, Gianluca, 2007. "A unifying framework for analysing common cyclical features in cointegrated time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 896-906, October.
    7. Dilip M. Nachane & Amlendu Dubey, 2021. "The Spectral Envelope: An Application to the Decoupling Problem in Economics," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 287-308, December.
    8. 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.
    9. del Barrio Castro, Tomás, 2021. "Testing for the cointegration rank between Periodically Integrated processes," MPRA Paper 106603, University Library of Munich, Germany, revised 2021.
    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

    Keywords

    Periodic autoregression; seasonality; high frequency data; cointegration; multicointegration; common features;
    All these keywords.

    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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