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Evaluation of the Forecast Models of Chinese Tourists to Thailand Based on Search Engine Attention: A Case Study of Baidu

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Abstract

Tourism, as a rather complex behaviour, includes lots of stages during the course of decision-making. Currently, related people have considered a lot of models for the tourism demand prediction. The research described in this paper aims at using the Baidu trends based on internet big data to construct inflow index of Chinese tourists to Thailand to provide forecasts on auxiliary. By limiting keywords set to only travel related keywords, dealing with keywords with different weights according to relations to series of interests, Baidu variable is constructed. We compare a number of standard models with Baidu-augmented models, and then evaluate if the variable of Baidu has raised these models’ prediction performances. We tested for the seasonal unit roots and the result confirmed that there were no seasonal unit roots. The evaluation result show models including Baidu variables can improve forecasting accuracy significantly and the choice of exogenous variables may critically affect prediction ability.

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References

  1. Vosen, S., & Schmidt, T. (2011). A monthly consumption indicator for Germany based on Internet search query data. Applied Economics Letters, 19(7), 683–687.

    Article  Google Scholar 

  2. Hand, C., & Judge, G. (2012). Searching for the picture: Forecasting UK cinema admissions using Google trends data. Applied Economics Letters, 19(11), 1051–1055.

    Article  Google Scholar 

  3. Smith, G. P. (2012). Google internet search activity and vola-tility prediction in the market for foreign currency. Finance Research Letters, 9(2), 103–110.

    Article  Google Scholar 

  4. Askitas, N., & Zimmermann, K. F. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, 55(2), 107–120.

    Article  Google Scholar 

  5. Artola, C., Pinto, F., & Pedraza, P. D. (2015). Can internet searches forecast tourism inflows? International Journal of Manpower, 36(1), 103–116.

    Article  Google Scholar 

  6. Li, G., Wong, K. K., Song, H., & Witt, S. F. (2006). Tourism demand forecasting: A time varying parameter error correc-tion model. Journal of Travel Research, 45(2), 175–185.

    Article  Google Scholar 

  7. Han, L., & Yonglian, W. (2016). Improving forecast accuracy based on mixed frequency forecasting model—A case study of inbound tourism. Journal of Intelligence, 35(9), 75–79.

    Google Scholar 

  8. Franses, P. H. (1991). Seasonality, non-stationarity and the forecasting of monthly time series. International Journal of Forecasting, 7, 199–208.

    Article  Google Scholar 

  9. Yan-hong, Y. A. N. G., & Qing, Z. E. N. G. (2013). Hepatitis B prediction model based on Google trends. Journal of Shanghai Jiaotong University, 33(2), 204–208.

    Google Scholar 

  10. Clements, M., & Galvao, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United states. Journal of Business and Economic Statistics, 26(4), 546–554.

    Article  MathSciNet  Google Scholar 

  11. Kewei, L., & Ying, C. (2007). Forecast of inbound tourists to China based on BP neural network and ARIMA combined model. Tourism Tribune, 22(4), 20–25.

    Google Scholar 

  12. Qian, D., Nana, S., & Wei, L. (2014). Real estate price prediction based on web search data. Statistical Research, 31(10), 81–88.

    Google Scholar 

  13. Chaitip, P., & Chaiboonsri, C. (2009). Forecasting with X-12-ARIMA and ARFIMA: International tourist arrivals to India. Annals of the University of Petrosani Economics, 9(3), 147–162.

    Google Scholar 

  14. Stock, J. H., & Watson, M. W. (2003). Forecasting output and inflation: The role of asset prices. Journal of Economic Literature, 419(3), 788–829.

    Article  Google Scholar 

  15. Haiyan, S., & Gang, L. (2008). Tourism demand modelling and forecasting: A review of recent research. Tourism Management, 29(2), 203–220.

    Article  Google Scholar 

  16. Goh, C., & Law, R. (2011). The methodological progress of tourism demand forecasting: A review of related literature. Journal of Travel & Tourism Marketing, 28(3), 296–317.

    Article  Google Scholar 

  17. Sun, Y., & Zhang, H. (2017). Forecast of tourism flow volume of tourist attraction based on degree of tourist attention of travel network: A case study of Baidu index of different clients. Human Geography, 32(3), 153–160.

    Google Scholar 

  18. Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454–464.

    Article  Google Scholar 

  19. Tao, H., & Wei, L. (2015). A study on the relationship between expectation and real housing prices based on query index. Statistics & Information Forum, 30, 49–53.

    MathSciNet  Google Scholar 

  20. Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397.

    Article  Google Scholar 

  21. Wilcox, J. A. (2007). Forecasting components of consumption with components of consumer sentiment. Business Economics, 42(4), 22–32.

    Article  Google Scholar 

  22. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 253–263.

    Google Scholar 

  23. Kvedaras, V., & Zemlys, V. (2012). Testing the functional constraints on parameters in regressions with variables of different frequency. Economics Letters, 116(2), 250–254.

    Article  MathSciNet  MATH  Google Scholar 

  24. Ghysels, E., Santa-Clare, P., & Valkanov, R. (2004). The MIDAS touch: Mixed data sampling regressions. Cirano Working Papers, 5(1), 512–517.

    Google Scholar 

  25. Park, S., & Lee, J. (2016). Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Journal of Travel & Tourism Marketing, 34(2016), 357–368.

    Google Scholar 

  26. van Veldhuizen, S., Vogt, B., & Voogt, B. (2016). Internet searches and transactions on the Dutch housing market. Applied Economics Letters, 23(18), 1321–1324.

    Article  Google Scholar 

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Authors and Affiliations

  1. Beijing International Studies University, Beijing, China

    Junjian Tang

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  1. Junjian Tang
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Correspondence to Junjian Tang.

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Tang, J. Evaluation of the Forecast Models of Chinese Tourists to Thailand Based on Search Engine Attention: A Case Study of Baidu. Wireless Pers Commun 102, 3825–3833 (2018). https://doi.org/10.1007/s11277-018-5413-2

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