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Note---Analysis of Time Series with Calendar Effects

Author

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  • Lon-Mu Liu

    (University of California, Los Angeles)

Abstract

Most national economic data and many marketing series are compiled monthly according to the Gregorian Calendar, but some of the ancient festivals or holidays, such as Easter, Jewish Passover, and Chinese New Year, are set by lunar calendar. Therefore the date of a holiday may vary between two adjacent months from year to year. Some product marketing and consumer behavior patterns are closely related to such holidays; the amount of monthly sales may change as the date of the holiday changes from one month to the other. This paper shows how calendar intervention influences the sample autocorrelation, both by a theoretical study and an actual example. Model estimation for a time series subject to calendar intervention is also discussed.

Suggested Citation

  • Lon-Mu Liu, 1980. "Note---Analysis of Time Series with Calendar Effects," Management Science, INFORMS, vol. 26(1), pages 106-112, January.
    • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:1:p:106-112
    DOI: 10.1287/mnsc.26.1.106
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    Cited by:

    1. Inglada, Vicente & Rey, Belén, 2004. "Spanish air travel and the September 11 terrorist attacks: a note," Journal of Air Transport Management, Elsevier, vol. 10(6), pages 441-443.
    2. Henryk Gurgul & Marcin Suder, 2013. "Modeling of withdrawals from selected ATMs of the "Euronet" network," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 13, pages 65-82.
    3. Gurgul Henryk & Suder Marcin, 2016. "Calendar and Seasonal Effects on the Size of Withdrawals from Atms Managed By Euronet," Statistics in Transition New Series, Statistics Poland, vol. 17(4), pages 691-722, December.
    4. Yue-Chune Lee & Ming-Chin Yang & Yu-Tung Huang & Chien-Hsiang Liu & Sun-Bing Chen, 2006. "Impacts of Cost Containment Strategies on Pharmaceutical Expenditures of the National Health Insurance in Taiwan, 1996–2003," PharmacoEconomics, Springer, vol. 24(9), pages 891-902, September.
    5. Henryk Gurgul & Marcin Suder, 2013. "The Properties of ATMs Development Stages - an Empirical Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 443-466, September.
    6. Liu, Lon-Mu & Bhattacharyya, Siddhartha & Sclove, Stanley L. & Chen, Rong & Lattyak, William J., 2001. "Data mining on time series: an illustration using fast-food restaurant franchise data," Computational Statistics & Data Analysis, Elsevier, vol. 37(4), pages 455-476, October.

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