Modelling and Forecasting Australian Domestic Tourism

By George Athanasopoulos, Rob J Hyndman. 2006

184 citations recorded by the source corpus.

In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models to forecast the domestic tourism demand. Combining these two frameworks, we build innovation state space models with exogenous variables. These models are able to capture the time series dynamics in the data, as well as economic and other relationships. We show that these models outperform alternative approaches for short-term forecasting and also produce sensible long-term forecasts. The forecasts are compared with the official Australian government forecasts, which are found to be more optimistic than our forecasts.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/0cd964b4a213b2ee48de3a43cf3c6c07a9905843

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Haiyan Song
2. D. Wu
3. Shujie Shen
4. Gang Li
5. Fateh Habibi
6. Pritee Ray
7. Ratri Parida
8. Sanjita Jaipuria

In Management Science and Operations Research

1. Gang Li
2. D. Wu
3. Haiyan Song
4. Shujie Shen
5. Amir F. Atiya
6. Alice H. Y. Hon
7. Fateh Habibi
8. Hisham El-Shishiny

In Sociology and Political Science

1. D. Wu
2. Haiyan Song
3. Shujie Shen
4. Gang Li
5. Fateh Habibi
6. Pritee Ray
7. Ratri Parida
8. Sanjita Jaipuria

Citations in the corpus

• Tourism and regional income inequality: Evidence from China (2016, result): “Consistent with the aforementioned conclusion, several studies focusing on Australia (Athanasopoulos & Hyndman, 2008), Peru (O’Hare & Barrett, 1999), South Africa (Cornelissen, 2005) and China (Bowden, 2005) have suggested that tourism spatial polarization, which is treated as enlarging the…”
• The determinants of Italian domestic tourism: A panel data analysis (2012, background): “Examples of studies estimating the role of relative prices and income are Seddighi and Shearing (1997), Garin-Munoz (2009), Taylor and Arigoni (2009), Bigano, Hamilton, and Tol (2006) and Athanasopoulos and Hyndman (2008). In particular, Seddighi and Shearing (1997) and Garin-Munoz (2009) find that ”
• The impacts of social and economic crises on tourist behaviour and expenditure: an evolutionary approach (2018, methodology): “In support of this finding, given that VFR tourists travel to see family and relatives, Ghaderi (2015) and Athanasopoulos and Hyndman (2008) point out that VFR travellers are not only resilient but also travel immediately after certain crises. The results indicated that the number of long-haul busin”
• New developments in tourism and hotel demand modeling and forecasting (2017, background): “Athanasopoulos and Hyndman (2008) found that combining the ISS model with exogenous variables captures time series dynamics well and outperforms the regression models.”
• The determinants of inbound tourism to Malaysia: a panel data analysis (2017, background): “Prices and tourist income are the most commonly used variables to explain tourism demand (e.g. Algieri, 2006; Athanasopoulos & Hyndman, 2008; Dritsakis, 2004; Hanly & Wade, 2007; Han, Durbarry, & Sinclair, 2006; Kulendran & Divisekera, 2007; Kulendran & Witt, 2003; Li et al., 2005; Massida & Etzo,…”
• The impact of COVID-19 on tourism sector in India (2020, methodology): “This paper considers two streams of literature: (1) impact of epidemic outbreak on tourism and (2) forecasting models used for predicting tourism demand.”
• Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, methodology): “Athanasopoulos and Hyndman (2008) considered a hybrid econometric/time series model for domestic Australian tourism, using a state space approach, while for the same problem of forecasting domestic Australian tourism, Athanasopoulos, Ahmed, and Hyndman (2009) considered a hierarchical forecasting mo”