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Accuracy and bias of experts’ adjusted forecasts

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  • Lin, Vera Shanshan
  • Goodwin, Paul
  • Song, Haiyan

Abstract

This study investigates whether experts’ group-based judgmental adjustments to econometric forecasts of tourism demand improve the accuracy of the forecasts and whether the adjusted forecasts are unbiased. The Delphi method was used to aggregate experts’ judgmental adjustments and a range of error measures and statistical tests were employed to evaluate forecast accuracy. Regression analysis was used to investigate whether the statistical and judgmentally-adjusted forecasts were unbiased. The hypothesis tests suggested that, on average, the adjustments of the Delphi panel improved forecast accuracy though the group-adjusted forecasts were found to be biased for some of the individual markets. In-depth interviews with the Delphi panellists provided further insights into the biases that were associated with the Delphi surveys.

Suggested Citation

  • Lin, Vera Shanshan & Goodwin, Paul & Song, Haiyan, 2014. "Accuracy and bias of experts’ adjusted forecasts," Annals of Tourism Research, Elsevier, vol. 48(C), pages 156-174.
    • Handle: RePEc:eee:anture:v:48:y:2014:i:c:p:156-174
    DOI: 10.1016/j.annals.2014.06.005
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    Cited by:

    1. Vera Shanshan Lin, 2019. "Judgmental adjustments in tourism forecasting practice: How good are they?," Tourism Economics, , vol. 25(3), pages 402-424, May.
    2. Toppinen, Anne & Röhr, Axel & Pätäri, Satu & Lähtinen, Katja & Toivonen, Ritva, 2018. "The future of wooden multistory construction in the forest bioeconomy – A Delphi study from Finland and Sweden," Journal of Forest Economics, Elsevier, vol. 31(C), pages 3-10.
    3. Liu, Anyu & Vici, Laura & Ramos, Vicente & Giannoni, Sauveur & Blake, Adam, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team," Annals of Tourism Research, Elsevier, vol. 88(C).
    4. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.
    5. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    6. Woodside, Arch G., 2020. "Interventions as experiments: Connecting the dots in forecasting and overcoming pandemics, global warming, corruption, civil rights violations, misogyny, income inequality, and guns," Journal of Business Research, Elsevier, vol. 117(C), pages 212-218.
    7. George Athanasopoulos & Rob J Hyndman & Mitchell O'Hara-Wild, 2021. "The Road to Recovery from COVID-19 for Australian Tourism," Monash Econometrics and Business Statistics Working Papers 1/21, Monash University, Department of Econometrics and Business Statistics.
    8. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    9. Zhang, Hanyuan & Song, Haiyan & Wen, Long & Liu, Chang, 2021. "Forecasting tourism recovery amid COVID-19," Annals of Tourism Research, Elsevier, vol. 87(C).

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