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A New Approach to Modelling and Forecasting Monthly Guest Nights in Hotels

Author

Listed:
  • Brännäs, Kurt

    (Department of Economics, Umeå University)

  • Hellström, Jörgen

    (Department of Economics, Umeå University)

  • Nordström, Jonas

    (Department of Economics, Umeå University)

Abstract

Starting from a day-to-day model on hotel specific guest nights we obtain an integer-valued moving average model by cross-sectional and temporal aggregation. The two parameters of the aggregate model reflect the daily mean check-in and the daily check-out probability. Letting the parameters be functions of dummy and economic variables we demonstrate the potential of the approach in terms of interesting interpretations. Empirical results are presented for a series of Norwegian guests in Swedish hotels. The results indicate strong seasonal patterns in both mean check-in and in the check-out probability. Models based on differenced series are preferred in terms of goodness-of-fit. In a forecast comparison the improvements due to economic variables is minute.

Suggested Citation

  • Brännäs, Kurt & Hellström, Jörgen & Nordström, Jonas, 1999. "A New Approach to Modelling and Forecasting Monthly Guest Nights in Hotels," Umeå Economic Studies 503, Umeå University, Department of Economics.
    • Handle: RePEc:hhs:umnees:0503
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    2. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    3. Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(3), pages 319-341, August.
    4. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    5. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    6. Kurt Brannas & A. M. M. Shahiduzzaman Quoreshi, 2010. "Integer-valued moving average modelling of the number of transactions in stocks," Applied Financial Economics, Taylor & Francis Journals, vol. 20(18), pages 1429-1440.
    7. Song, Haiyan & Lin, Shanshan & Witt, Stephen F. & Zhang, Xinyan, 2011. "Impact of financial/economic crisis on demand for hotel rooms in Hong Kong," Tourism Management, Elsevier, vol. 32(1), pages 172-186.
    8. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
    9. Luis Alberiko Gil-Alaña, 2010. "Tourism in South Africa. Time series persistence and the nature of shocks. Are they transitory or permament?," NCID Working Papers 06/2011, Navarra Center for International Development, University of Navarra.
    10. J. Cunado & L.A. Gil-Alana & F. P Erez de Gracia, 2008. "Fractional Integration and Structural Breaks: Evidence from International Monthly Arrivals in the USA," Tourism Economics, , vol. 14(1), pages 13-23, March.
    11. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    12. Bahman Rostami‐Tabar & M. Zied Babai & Aris Syntetos & Yves Ducq, 2013. "Demand forecasting by temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(6), pages 479-498, September.
    13. Dungey Mardi & Martin Vance L. & Tang Chrismin & Tremayne Andrew, 2020. "A threshold mixed count time series model: estimation and application," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-18, April.
    14. J. Cunado & L.A. Gil-Alana & F. Pérez de Gracia, 2004. "Modelling Monthly Spanish Tourism: A Seasonal Fractionally Integrated Approach," Tourism Economics, , vol. 10(1), pages 79-94, March.
    15. Adam G. Walke & Thomas M. Fullerton Jr., 2019. "Metropolitan Hotel Sector Forecast Accuracy in El Paso," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(2), pages 179-191, June.
    16. Quoreshi, Shahiduzzaman, 2005. "Modelling High Frequency Financial Count Data," Umeå Economic Studies 656, Umeå University, Department of Economics.
    17. Juan Luis Eugenio-Martín & Noelia Martín Morales & Riccardo Scarpa, 2004. "Tourism and Economic Growth in Latin American Countries: A Panel Data Approach," Working Papers 2004.26, Fondazione Eni Enrico Mattei.
    18. Luis A. Gil-Alana & Juncal Cunado & Fernando Perez de Gracia, 2008. "Tourism in the Canary Islands: forecasting using several seasonal time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 621-636.
    19. Brännäs, Kurt & Lönnbark, Carl, 2006. "Effects of Explanatory Variables in Count Data Moving Average Models," Umeå Economic Studies 679, Umeå University, Department of Economics.
    20. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.
    21. J. Cunado & L.A. Gil-Alana & F. Péarez de Gracia, 2005. "The Nature of Seasonality in Spanish Tourism Time Series," Tourism Economics, , vol. 11(4), pages 483-499, December.
    22. Darolles, Serge & Fol, Gaëlle Le & Lu, Yang & Sun, Ran, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 181-203.

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    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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