Modeling Seasonal Variation in Tourism Flows with Climate Variables

Abstract

The purpose of this article is to model and forecast the seasonal variation, the fluctuations in tourist numbers from season to season in Australian inbound holiday tourism, using climate variables such as maximum temperature, humidity, and hours of sunshine. For estimation purposes this study uses quarterly data on arrivals from the US, UK, Japan, and New Zealand to Australia from September 1975 to September 2009. Seasonal variation, which is the respective and predictable movement of visitation around the trend line, was first extracted from the quarterly holiday tourist arrivals timeseries using the Basic Structural Model (BSM) approach. Subsequently, the influence of climate variables on seasonal variation in different seasons was identified using the average euclidean minimum distance (AD) statistics. The AD statistics show that climate variables shape the characteristic of seasonal variation of tourism flows but the effect tends to vary between seasons and countries. A time-series model was estimated with climate variables to forecast seasonal variation. The forecasting comparison result shows that climate variables improve the forecasting performance. The approach can be replicated to help destination managers and forecasters determine if climate variables influence tourism flows between other origins and destinations globally.

An Inter-Comparison of the Holiday Climate Index (HCI:Beach) and the Tourism Climate Index (TCI) to Explain Canadian Tourism Arrivals to the Caribbean

“…Research has found that the outbound tourism demand is also sensitive to seasonal and inter-annual climate variability, variably pushing demand from home to destination countries [23,[56][57][58][59][60]. Because virtually all applications of climate indices for tourism have assessed the conditions at the destination (i.e., climate-pull factor) without climate-push factors at source markets, an opportunity also exists to test the relationship between climate indices and source markets to understand better how climate-push factors might drive arrivals to a destination that is climatically different.…”

Statistical Effect of Temperature on Tourism Seasonality in Romania.

“…The aim of our study was not only to identify if there are any changes in tourism seasonality for different locations in Romania, but also to examine how the changes in temperatures regime influences the seasonality of tourism, as such studies has been performed before (Kulendran and Dwyer, 2012). For this we applied standard correlation and backward stepwise multiple linear regression analysis (Myers, 1990) which can identify the most important predictors in a linear model using Akaike…”

Analyzing the Impact of Climatic and Economic Variables on Tourism Demand Fluctuation in Japan

“…However, because some causes of tourism seasonality, which are related to natural-and socioeconomic factors, do not correspond with each other, various strategies and individual evaluations should be determined. On the other hand, Kulendran and Dwyer (2010;2012) focus on economic factors, in addition to climatic elements, when considering the influence of seasonal fluctuations on tourism demand.…”