This article attempts to bring an economic underpinning to tourism research. It uses the gravity model to derive an econometric model to explore the relationship between cultural similarity and tourism demand, with special reference to Australia inbound tourism from 42 source countries.
Since language and religion are thought to be the main exposition and carrier of culture, we developed a continuous, normalized, and time variant index to capture the similarity in language and religious profile between a source country and Australia. The inclusion of these indexes in an empirical
model yields OLS and quantile results that support the belief that there is a close link between culture similarity and tourism demand.
This paper adopts a model-free approach to forecasting monthly international tourist arrivals to Australia from four major origin countries: New Zealand, UK, the USA, and China. While most researchers use parametric methodologies to model tourism demand, this study proposes a non-parametric approach that employs a 'Partitive Simulation Process' or PASIP by partitioning the original monthly time series into 12 sub-series according to the month. Both ordinary and time-weighted non-parametric bootstraps are used, to resample the observed samples 2,000 times, to estimate the underlying population statistics. We then conduct PASIP's forecasts and compare them with ARIMA forecasts. There are four significant observations. First, the partitive process is a viable way of handling seasonality. Second, PASIP performs well in predicting the turning points and data trends. Third, weighted PASIP generally outperforms non-weighted PASIP in terms of forecast errors. Fourth, PASIP produces smaller forecast errors than ARIMA for UK, USA, and China data.
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