Forecasting international tourism demand: a local spatiotemporal model

Jiao, Xiaoying, Li, Gang and Chen, Jason Li (2020) Forecasting international tourism demand: a local spatiotemporal model Annals of Tourism Research, 83, 102937.

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Abstract

This study investigates whether tourism forecasting accuracy is improved by incorporating spatial dependence and spatial heterogeneity. One- to three-step-ahead forecasts of tourist arrivals were generated using global and local spatiotemporal autoregressive models for 37 European countries and the forecasting performance was compared with that of benchmark models including autoregressive moving average, exponential smoothing and Naïve 1 models. For all forecasting horizons, the two spatial models outperformed the non-spatial models. The superior forecasting performance of the local model suggests that the full reflection of spatial heterogeneity can improve the accuracy of tourism forecasting.

Item Type:	Article
Divisions :	Faculty of Arts and Social Sciences > Surrey Business School
Authors :	Name Email ORCID Jiao, Xiaoying Li, Gang G.Li@surrey.ac.uk Chen, Jason Li l.chen@surrey.ac.uk
Date :	July 2020
DOI :	10.1016/j.annals.2020.102937
Copyright Disclaimer :	© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords :	Tourism demand; Spatial spillover; Spatial heterogeneity; Panel; Forecasting; Local estimation
Depositing User :	Clive Harris
Date Deposited :	10 Jun 2020 17:25
Last Modified :	10 Jun 2020 17:25
URI:	http://epubs.surrey.ac.uk/id/eprint/857772

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