Forecasting with stable seasonal pattern models with an application to hawaiian tourism data

Rong Chen, Thomas B. Fomby

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

We propose several variations of the stable seasonal pattern (SSP) model first introduced by Marshall and Oliver and study their prediction procedures. Depending on the type of data (count data or continuous variable), we propose different treatments. Previously SSP models have been applied to trendless data or adapted to trending data in ad hoc ways. In the models considered here, conditional independence allows the seasonal pattern and trend to be modeled separately, whereas prediction uses both efficiently. In an out-of-sample forecasting experiment conducted on Hawaiian tourism data, one of the proposed variations demonstrates its long-term forecasting potential relative to seasonal Box-Jenkins autoregressive integrated moving average and transfer function models.

Original languageEnglish (US)
Pages (from-to)497-504
Number of pages8
JournalJournal of Business and Economic Statistics
Volume17
Issue number4
DOIs
StatePublished - Jan 1 1999

Fingerprint

Tourism
Forecasting
Conditional Independence
Count Data
Prediction
Moving Average
Continuous Variables
Model
Transfer Function
Demonstrate
experiment
Experiment
trend

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Keywords

  • Conditional Poisson model
  • Forecasting
  • Seasonality

Cite this

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Forecasting with stable seasonal pattern models with an application to hawaiian tourism data. / Chen, Rong; Fomby, Thomas B.

In: Journal of Business and Economic Statistics, Vol. 17, No. 4, 01.01.1999, p. 497-504.

Research output: Contribution to journalArticle

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