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Modelling Seasonality and Structural Breaks: Visitors to NZ and 9/11

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Statistical Modelling in Biostatistics and Bioinformatics

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

We demonstrate the poor performance, with seasonal data, of existing methods for endogenously dating multiple structural breaks. Motivated by iterative nonparametric techniques, we present a new approach for estimating parametric structural break models that perform well. We suggest that iterative estimation methods are a simple but important feature of this approach when modelling seasonal data. The methodology is illustrated by simulation and then used for an analysis of monthly short-term visitor arrival time series to New Zealand, to assess the effect of the 9/11 terrorist attacks. While some historical events had a marked structural effect on trends in those arrivals, we show that 9/11 did not.

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Acknowledgements

Statistics New Zealand kindly supplied the data. We thank those who commented on presentations at Statistics New Zealand, the Reserve Bank of New Zealand, Victoria Management School, the ASC/NZSA 2006 Conference, and the TSEFAR 2006 Conference. We also thank Peter Thomson for some helpful suggestions that improved the paper.

Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, 600, Wellington, New Zealand

    John Haywood

  2. School of Economics and Finance, Victoria University of Wellington, Wellington, New Zealand

    John Randal

Authors
  1. John Haywood
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  2. John Randal
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Corresponding author

Correspondence to John Haywood .

Editor information

Editors and Affiliations

  1. Centre of Biostatistics, University of Limerick, Limerick, Ireland

    Gilbert MacKenzie

  2. Centre of Biostatistics, University of Limerick, Limerick, Ireland

    Defen Peng

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Haywood, J., Randal, J. (2014). Modelling Seasonality and Structural Breaks: Visitors to NZ and 9/11. In: MacKenzie, G., Peng, D. (eds) Statistical Modelling in Biostatistics and Bioinformatics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-04579-5_6

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