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Research Note: Forecasting Tourism Demand by Disaggregated Time Series – Empirical Evidence from Spain

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  • Glauber Eduardo de Oliveira Santos

    (Department of Tourism and Hospitality, Federal Institute of São Paulo (IFSP), R. Pedro Vicente, 625, São Paulo, SP, 01109-010, Brazil)

Abstract

Frequently, tourism demand can be disaggregated into different components according to variables such as country of residence, purpose of the trip, type of transport and accommodation. However, researchers generally develop forecasts of the total tourism demand without considering the existence of its disaggregated components, which might have independent behaviour. As an alternative, the disaggregated approach models each component first and then sums these individual forecasts in order to obtain aggregate forecasts of the total tourism demand. This paper compares the aggregated and disaggregated approaches by ex post forecasting international tourist arrivals in Spain. The total tourism demand is disaggregated into 12 different origins. The HEGY test is used to check for regular and seasonal unit roots in each time series and SARIMA models are used to develop single forecasts. This empirical study finds slightly more accurate results using the disaggregated approach in multi-step, out-of-sample forecasting.

Suggested Citation

  • Glauber Eduardo de Oliveira Santos, 2009. "Research Note: Forecasting Tourism Demand by Disaggregated Time Series – Empirical Evidence from Spain," Tourism Economics, , vol. 15(2), pages 467-472, June.
    • Handle: RePEc:sae:toueco:v:15:y:2009:i:2:p:467-472
    DOI: 10.5367/000000009788254278
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    References listed on IDEAS

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    1. Palm, F. & Zellner, A., 1991. "To combine or not to combine? issues of combining forecasts," LIDAM Discussion Papers CORE 1991022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Edwards, John B & Orcutt, Guy H, 1969. "Should Aggregation Prior to Estimation Be the Rule?," The Review of Economics and Statistics, MIT Press, vol. 51(4), pages 409-420, November.
    3. Antoni Espasa & Rebeca Albacete, 2007. "Econometric modelling for short-term inflation forecasting in the euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 303-316.
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    1. Ioannis Chatziantoniou & Stavros Degiannakis & Bruno Eeckels & George Filis, 2016. "Forecasting tourist arrivals using origin country macroeconomics," Applied Economics, Taylor & Francis Journals, vol. 48(27), pages 2571-2585, June.

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