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UAL Research Online

Forecasting tourism growth with State-Dependent Models

Guan, B. and Silva, E.S. and Hassani, H. and Heravi, S. (2022) Forecasting tourism growth with State-Dependent Models. Annals of Tourism Research, 94. ISSN 0160-7383

Type of Research: Article
Creators: Guan, B. and Silva, E.S. and Hassani, H. and Heravi, S.

We introduce two forecasting methods based on a general class of non-linear models called ‘State-Dependent Models’ (SDMs) for tourism demand forecasting. Using a Monte Carlo simulation which generated data from linear and non-linear models, we evidence how estimations from SDMs can capture the level shifts pattern and nonlinearity in data. Next, we apply two new forecasting methods based on SDMs to forecast tourism demand growth in Japan. The forecasts are compared with classical recursive SDM forecasting, Naïve forecasting, ARIMA, Exponential Smoothing, Neural Network models, Time varying parameters, Smooth Transition Autoregressive models, and with a linear regression model with two dummy variables. We find that improvements in forecasting with the proposed SDM-based forecasting methods are more pronounced in the longer-term horizons.

Official Website: https://www.sciencedirect.com/science/article/abs/pii/S0160738322000366
Keywords/subjects not otherwise listed: State-Dependent Models, tourism demand, forecasting, non-linear, Japan
Publisher/Broadcaster/Company: Elsevier
Your affiliations with UAL: Colleges > London College of Fashion
Date: 24 March 2022
Digital Object Identifier: 10.1016/j.annals.2022.103385
Date Deposited: 14 Apr 2022 12:50
Last Modified: 14 Apr 2022 12:50
Item ID: 18087
URI: https://ualresearchonline.arts.ac.uk/id/eprint/18087

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