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on Tourism Economics |
| By: | Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona) |
| Abstract: | This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks. |
| Keywords: | tourism demand, forecasting, multivariate, multiple-output, artificial neural networks JEL classification: C22, C45, C63, L83, R11 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201502&r=tur |
| By: | Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona) |
| Abstract: | This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting. |
| Keywords: | Forecasting, support vector regressions, artificial neural networks, tourism demand, Spain JEL classification: C02, C22, C45, C63, E27, R11 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201507&r=tur |
| By: | Bianca Biagi (University of Sassari, CRENoS); Maria Gabriela Ladu (University of Sassari, CRENoS); Vicente Royuela (Faculty of Economics, University of Barcelona) |
| Abstract: | The analysis of the relationship between tourism and human development points to a positive link between these activities, basically by means of the improvement of economic conditions. In the present study we analyze whether and to what extent this relationship remains positive under different circumstances. We examine a selection of 63 countries from 1996 to 2008 and consider the Human Development Index plus a composite indicator of the tourism market as a whole. Findings confirm that, on average, tourism is positively associated with human development, particularly education (i.e., literacy rate), although the association may be affected by circumstances. |
| Keywords: | Human Development Index, tourism development, capability approach, externalities JEL classification: 015, 010, D62 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201505&r=tur |
| By: | Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona) |
| Abstract: | This study aims to analyze the effects of data pre-processing on the performance of forecasting based on neural network models. We use three different Artificial Neural Networks techniques to forecast tourist demand: a multi-layer perceptron, a radial basis function and an Elman neural network. The structure of the networks is based on a multiple-input multiple-output setting (i.e. all countries are forecasted simultaneously). We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels. |
| Keywords: | artificial neural networks, forecasting, multiple-input multiple-output (MIMO), seasonality, detrending, tourism demand, multilayer perceptron, radial basis function, Elman JEL classification: L83, C53, C45, R11 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201503&r=tur |
| By: | Peter Haxton |
| Abstract: | A key issue for OECD countries is to understand how to strengthen the position of the destination, how to be more effective in supporting a stronger, more inclusive and sustainable tourism growth and how to further improve its competitiveness in the global tourism market. This report examines the changing global trends and inter-linked policy challenges, then reviews the policy framework supporting tourism growth and presents various policy perspectives, detailing how they inter-connect and support tourism growth. The report explores ways for closer policy integration between tourism and related policy areas and suggests new policy approaches to more effectively support tourism growth. The report was considered as approved by the OECD Tourism Committee as of 13 January 2015. |
| Date: | 2015–03–11 |
| URL: | http://d.repec.org/n?u=RePEc:oec:cfeaab:2015/1-en&r=tur |