Forecasting tourism growth with state-dependent models

Guan, Bo ORCID: https://orcid.org/0000-0001-9764-5646, Silva, Emmanuel Sirimal, Hassani, Hossein and Heravi, Saeed ORCID: https://orcid.org/0000-0002-0198-764X 2022. Forecasting tourism growth with state-dependent models. Annals of Tourism Research 94 , 103385. 10.1016/j.annals.2022.103385

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier Masson
ISSN:	0160-7383
Date of First Compliant Deposit:	16 March 2022
Date of Acceptance:	5 March 2022
Last Modified:	09 May 2023 02:51
URI:	https://orca.cardiff.ac.uk/id/eprint/148421

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