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Forecasting Tourism Demand with Composite Indicator Approach for Fiji

Author

Listed:
  • Ann-Ni Soh
  • Chin-Hong Puah
  • M. Affendy Arip

Abstract

This study attempts to scrutinize the fluctuations of the Fijian tourism market and forecast the early warning signals of tourism market vulnerability using the tourism composite indicator (TCI). The data employed on a monthly basis from 2000M01 to 2017M12 and the indicator construction steps were adopted from the ideology of the National Bureau of Economic Research (NBER). A parsimonious macroeconomic and non-economic fundamental determinant are included for the construction of TCI. Subsequently, the procedure then employed the seasonal adjustment using Census X-12, Christiano-Fitzgerald filtering approach, and Bry-Boschan dating algorithm. Empirical evidence highlighted the signalling attributes against Fijian tourism demand with an average lead time of 2.75 months and around 54 percent of directional accuracy rate, which is significant at 5 percent significance level. Thus, the non-parametric technique can forecast the tourism market outlook and the constructed TCI can provide information content from a macroeconomic perspective for policymakers, tourism market players and investors.

Suggested Citation

  • Ann-Ni Soh & Chin-Hong Puah & M. Affendy Arip, 2019. "Forecasting Tourism Demand with Composite Indicator Approach for Fiji," Business and Economic Research, Macrothink Institute, vol. 9(4), pages 12-22, December.
    • Handle: RePEc:mth:ber888:v:9:y:2019:i:4:p:12-22
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    References listed on IDEAS

    as
    1. Andrea Saayman & Ilsé Botha, 2017. "Non-linear models for tourism demand forecasting," Tourism Economics, , vol. 23(3), pages 594-613, May.
    2. Puah, Chin-Hong & Huan, Suk-Hie & Thien, Fung-Thai, 2018. "Determinants of Chinese demand for tourism in Malaysia," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 14(3), May.
    3. Chin-Hong Puah & Meng-Chang Jong & Norazirah Ayob & Shafinar Ismail, 2021. "The Impact of Tourism on the Local Economy in Malaysia," International Journal of Business and Management, Canadian Center of Science and Education, vol. 13(12), pages 151-151, July.
    4. Wong, Shirly Siew-Ling & Puah, Chin-Hong & Abu Mansor, Shazali & Liew, Venus Khim-Sen, 2012. "Early warning indicator of economic vulnerability," MPRA Paper 39944, University Library of Munich, Germany.
    5. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    6. Shirly Siew-Ling WONG & Chin-Hong PUAH & Shazali ABU MANSOR & Venus Khim-Sen LIEW, 2016. "Measuring Business Cycle Fluctuations: An Alternative Precursor To Economic Crises," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 235-248.
    7. Ann-Ni Soh & Chin-Hong Puah & M. Affendy Arip & Tai-Hock Kuek, 2019. "Oil Price and Fijian Tourism Cycle: A Markov Regime-switching Model," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 188-192.
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    Cited by:

    1. Ann-Ni Soh & Chin-Hong Puah & Meng-Chang Jong, 2022. "Macroeconomic Determinants of Tourism Demand in Malaysia: A Markov Switching Regression Approach," Business Management and Strategy, Macrothink Institute, vol. 13(2), pages 95-107, December.
    2. El houssin Ouassou & Hafsa Taya, 2022. "Forecasting Regional Tourism Demand in Morocco from Traditional and AI-Based Methods to Ensemble Modeling," Forecasting, MDPI, vol. 4(2), pages 1-18, April.
    3. Jong, Meng-Chang & Soh, Ann-Ni, 2021. "Responsible Recovery from COVID-19: An Empirical Overview of Tourism Industry," MPRA Paper 107661, University Library of Munich, Germany.
    4. Jong, Meng-Chang, 2020. "Empirical Review on Tourism Demand and COVID-19," MPRA Paper 103919, University Library of Munich, Germany.

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    More about this item

    Keywords

    Tourism composite indicator; Early warning signal; Non-parametric approach; Tourism demand forecasting;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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