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Forecasting Tourism-Generated Employment: The Case of Denmark

Author

Listed:
  • Stephen F. Witt

    (School of Management, University of Surrey, Guildford, Surrey GU2 7XH, UK)

  • Haiyan Song

    (School of Management, University of Surrey, Guildford, Surrey GU2 7XH, UK)

  • Stephen Wanhill

    (International Centre for Tourism and Hospitality Research, Bournemouth University, Talbot Campus, Fern Barrow, Poole, BH12 5BB, UK)

Abstract

The empirical results from a forecasting competition show that the unrestricted vector autoregressive model is likely to generate the most accurate forecasts of international tourist expenditure in Denmark. This model is therefore estimated (using data for 1969–99) and is used to generate tourism expenditure forecasts for Denmark to 2010. The employment requirements (direct, indirect and induced) associated with these expenditure forecasts are then estimated using an input–output model. The forecasts of employment demands are shown across all industrial sectors, and linked to qualifications data in respect of the labour force. The major impacts of foreign tourist expenditure on employment in Denmark occur in the retail, hotel and restaurant sectors. Foreign tourist expenditure is also significantly associated with graduate employment.

Suggested Citation

  • Stephen F. Witt & Haiyan Song & Stephen Wanhill, 2004. "Forecasting Tourism-Generated Employment: The Case of Denmark," Tourism Economics, , vol. 10(2), pages 167-176, June.
    • Handle: RePEc:sae:toueco:v:10:y:2004:i:2:p:167-176
    DOI: 10.5367/000000004323142407
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    References listed on IDEAS

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    1. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    2. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
    3. Lindsay W. Turner & Stephen F. Witt, 2001. "Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models," Tourism Economics, , vol. 7(2), pages 135-147, June.
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    Cited by:

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    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. KETENCI, Natalya, 2010. "Cointegration Analysis Of Tourism Demand For Turkey," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 10(1).
    4. Ketenci, Natalya, 2009. "The ARDL Approach to Cointegration Analysis of Tourism Demand in Turkey: with Greece as the substitution destination," MPRA Paper 86602, University Library of Munich, Germany.
    5. Jong, Meng-Chang & Hong, Puah & Arip, Mohammad Affendy, 2020. "Modelling Tourism Demand: An Augmented Gravity Model," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 54(2), pages 105-112.
    6. Apostolos Ampountolas, 2019. "Forecasting hotel demand uncertainty using time series Bayesian VAR models," Tourism Economics, , vol. 25(5), pages 734-756, August.
    7. Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
    8. Wenxing Lu & Jieyu Jin & Binyou Wang & Keqing Li & Changyong Liang & Junfeng Dong & Shuping Zhao, 2020. "Intelligence in Tourist Destinations Management: Improved Attention-based Gated Recurrent Unit Model for Accurate Tourist Flow Forecasting," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    9. Petrevska, Biljana, 2012. "Forecasting International Tourism Demand: The Evidence Of Macedonia," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 3(1), pages 45-55.
    10. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    11. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
    12. Kazutaka Kurasawa, 2016. "Chinese Economic Growth and Visitors to Japan: A Bivariate Cointegration Analysis," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 4(4), pages 168-179, December.
    13. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.

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