Forecasting tourism growth with State-Dependent Models
Datos Bibliográficos
| ID | 11233474 |
|---|---|
| Autores | Bo Guan (0000-0001-7014-9941, Cardiff University), Emmanuel Sirimal Silva (0000-0003-3851-9230, University of the Arts London, autor de correspondencia), Hamidreza Hassani (0000-0003-0897-8663, University of Tehran), Hossein Hassani (0000-0003-3979-9166), Saeed Heravi (0000-0002-0198-764X, Cardiff University) |
| Año | 2022 |
| Volumen | 94 |
| Páginas | 103385 |
| Fecha de publicación | 2022-05-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Annals of Tourism Research (JOURNAL) |
| Identificadores de la revista | ISSN: 0160-7383 • E-ISSN: 1873-7722 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.annals.2022.103385 |
| OpenAlex | W4220711521 |
| Idioma | EN |
| Citas recibidas | 4 |
| Referencias citadas | 36 |
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
Autoregressive integrated moving average · Autoregressive model · Econometrics · Economics · Exponential smoothing · Geography · Linear model · Linear regression · Machine learning · Nonlinear system · Smoothing · Time series · Tourism · Computer Science · Diverse Aspects of Tourism Research · Grey System Theory Applications · Wine Industry and Tourism
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| Obras citantes distintas | 4 |
|---|---|
| Citas por año | 2 |
| Intervalo de citas | 2024 - 2026 (3) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 4 |