Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data
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 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.(This abstract was borrowed from another version of this item.)
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Bibliographic Info
Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 25 (2007)
Issue (Month): (January)
Pages: 21-32
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Related research
Keywords:Other versions of this item:
- 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 2005-3, School of Economics and Management, University of Aarhus.
- 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
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- del Barrio Castro, Tom s & Osborn, Denise R., 2008.
"Cointegration For Periodically Integrated Processes,"
Econometric Theory,
Cambridge University Press, vol. 24(01), pages 109-142, February.
- Tomas del Barrio Castro & Denise R Osborn, 2005. "Cointegration for Periodically Integrated Processes," The School of Economics Discussion Paper Series 0522, Economics, The University of Manchester.
- 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.
- Marco Centoni & Gianluca Cubadda, 2011. "Modelling Comovements of Economic Time Series: A Selective Survey," CEIS Research Paper 215, Tor Vergata University, CEIS, revised 26 Oct 2011.
- Gianluca Cubadda, 2007.
"A Unifying Framework for Analysing Common Cyclical Features in Cointegrated Time Series,"
CEIS Research Paper
102, Tor Vergata University, CEIS.
- 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.
- 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, 07.
- Doucouliagos , H. & Paldam, M., 2007. "The aid effectiveness literature: The sad results of 40 years of research," Cambridge Working Papers in Economics 0773, Faculty of Economics, University of Cambridge.
- Hristos Doucouliagos & Martin Paldam, 2005. "The Aid Effectiveness Literature. The Sad Result of 40 Years of Research," Economics Working Papers 2005-15, School of Economics and Management, University of Aarhus.
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