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  • 1.
    Brännäs, Kurt
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Prediction and control for a time-series count data model1995In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 11, no 2, p. 263-270Article in journal (Refereed)
    Abstract [en]

    Time series of count data are becoming more widely available. In a recently suggested class of models, the serial correlation between counts can conveniently be accounted for. In this paper, an easily calculated linear predictor is introduced. Control solutions for average count and for probabilities of specified events are given. An illustration based on a road accident frequency model for a Swedish county is included.

  • 2.
    Brännäs, Kurt
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Hellström, Jörgen
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Nordström, Jonas
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    A new approach to modelling and forecasting monthly guest nights in hotels2002In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 18, no 1, p. 19-30Article in journal (Refereed)
    Abstract [en]

    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 mean check-in and the 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 are small. © 2002 International Institute of Forecasters. Published by Elsevier Science B.V.

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