Fieger, Peter and Rice, John (2016): Modelling Chinese Inbound Tourism Arrivals into Christchurch.
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Abstract
New data and modelling approaches are improving the usefulness of internet search data for forecasting inbound tourist arrivals. This short paper provides evidence of the usefulness of Baidu search data in predicting Chinese inbound tourist arrivals into a specific region in New Zealand. It also compares three modelling approaches, finding a Vector Autoregressive approach the most useful.
Item Type: | MPRA Paper |
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Original Title: | Modelling Chinese Inbound Tourism Arrivals into Christchurch |
Language: | English |
Keywords: | Tourism forecasting, Applied Econometrics |
Subjects: | D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis L - Industrial Organization > L8 - Industry Studies: Services > L83 - Sports ; Gambling ; Restaurants ; Recreation ; Tourism R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics |
Item ID: | 75468 |
Depositing User: | Peter Fieger |
Date Deposited: | 07 Dec 2016 14:26 |
Last Modified: | 04 Oct 2019 21:20 |
References: | 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. Box, G. & Jenkins, G. (1968). Some recent advances in forecasting and control. Applied Statistics, 17(2), 91-109. Hamilton, J. D., (1994) Time Series Analysis, Princeton, Princeton University Press. Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. Watson, M. W., (1994), Vector Autogregression and Co-integration. In Vol. IV of Handbook of Econometrics, ed. R.F. Engle and D.L. McFadden. Amsterdam: Elsevier. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342. Zhou-Grundy, Y., & Turner, L. W. (2014). The Challenge of Regional Tourism Demand Forecasting: The Case of China. Journal of Travel Research, DOI: 0047287513516197. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/75468 |