Modeling seasonality in tourism forecasting

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Kulendran, Nada and Wong, Kevin K. F (2005) Modeling seasonality in tourism forecasting. Journal of Travel Research, 44 (2). pp. 163-170. ISSN 1552-6763 Online 0047-2875 Print

Abstract

Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, RIMA14 and ARIMA1. ARIMA14 is used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity. ARIMA1 considers the series only in first differences, and seasonality is modeled with a constant and three seasonal dummies. The selection of either model depends on the nature of seasonality. Conventional unit root tests determine the nature of seasonality and the order of integration and, therefore, the series' choice of forecasting model. To determine whether the test correctly identifies the forecasting model for tourism demand, out-of-sample forecasting performance of ARIMA1 and ARIMA14 is compared with HEGY unit root model selection method. Comparing forecasting performance of both models with HEGY unit root model selection shows that the outcome of HEGY test procedure may not be useful in the selection of a univariate time-series model for quarterly tourism demand series.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/1776
DOI https://doi.org/10.1177/0047287505276605
Official URL http://dx.doi.org/10.1177/0047287505276605
Subjects Historical > Faculty/School/Research Centre/Department > School of Economics and Finance
Historical > RFCD Classification > 340000 Economics
Historical > RFCD Classification > 350000 Commerce, Management, Tourism and Services
Keywords deterministic seasonality, stochastic non-stationary seasonality, measures of seasonal variation, multiplicative seasonal ARIMA model, unit root test, out-of-sample forecast accuracy, model selection
Citations in Scopus 74 - View on Scopus
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