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on Tourism Economics |
| By: | Johan Fourie (Department of Economics, University of Stellenbosch); Jaume Roselló (Department of Applied Economics, University Of The Balearic Islands); Maria Santana-gallego (Department of Applied Economics, University Of The Balearic Islands) |
| Abstract: | Religious beliefs influence many aspects of peoples’ daily lives, so it is plausible to argue that religion affects some of humanity’s most central endeavors, such as trade, migration, foreign investment and tourism. This paper investigates the role a country’s religious affiliation plays in destination choice for international tourism. To that end, a gravity model for international tourist arrivals is estimated by using a dataset of 164 countries for the period 1995-2010. Results provide evidence that religious similarity has significant explanatory power in global tourism flows even after controlling for other measures of cultural affinity. Moreover, the presence of common religious minorities in the country has a positive impact on tourism flows. However, although religious pluralism foster tourism flows between countries, religious similarity has a stronger positive effect. |
| Keywords: | religion, tourism demand, gravity model |
| JEL: | A13 L83 Z12 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:sza:wpaper:wpapers214&r=tur |
| By: | Chang, C-L.; Hsu, H-K.; McAleer, M.J. |
| Abstract: | __Abstract__ The paper uses monthly data on financial stock index returns, tourism stock sub-index returns, effective exchange rate returns and interest rate differences from April 2005 – August 2013 for Taiwan that applies Chang’s (2014) novel approach for constructing a tourism financial indicator, namely the Tourism Financial Conditions Index (TFCI). The TFCI is an adaptation and extension of the widely-used Monetary Conditions Index (MCI) and Financial Conditions Index (FCI) to tourism stock data. However, the method of calculation of the TFCI is different from existing methods of constructing the MCI and FCI in that the weights are estimated empirically. The empirical findings show that TFCI is estimated quite accurately using the estimated conditional mean of the tourism stock index returns. The new TFCI is straightforward to use and interpret, and provides interesting insights in predicting the current economic and financial environment for tourism stock index returns that are based on publicly available information. In particular, the use of market returns on the tourism stock index as the sole indicator of the tourism sector, as compared with the general activity of economic variables on tourism stocks, is shown to provide an exaggerated and excessively volatile explanation of tourism financial conditions. |
| Keywords: | Monetary Conditions Index, Financial Conditions Index, model-based tourism, unbiased estimation |
| JEL: | B41 E44 E47 G32 |
| Date: | 2014–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:51314&r=tur |
| By: | Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona) |
| Abstract: | This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an Elman neural network. We use official statistical data of inbound international tourism demand to Catalonia (Spain) from 2001 to 2012. By means of cointegration analysis we find that growth rates of tourist arrivals from all different countries share a common stochastic trend, which leads us to apply a multivariate out-of-sample forecasting comparison. When comparing the forecasting accuracy of the different techniques for each visitor market and for different forecasting horizons, we find that radial basis function models outperform multi-layer perceptron and Elman networks. We repeat the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results, and we find no significant differences when additional lags are incorporated. These results reveal the suitability of hybrid models such as radial basis functions that combine supervised and unsupervised learning for economic forecasting with seasonal data. |
| Keywords: | forecasting; tourism demand; cointegration; multiple-output; artificial neural networks. JEL classification: L83; C53; C45; R11 |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201417&r=tur |