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on Tourism Economics |
| By: | Oscar Claveria (Department of Econometrics. University of Barcelona ); Enric Monte (Department of Signal Theory and Communications. Polytechnic University of Catalunya. ); Salvador Torra (Department of Econometrics & Riskcenter-IREA. Universitat de 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:aqr:wpaper:201502&r=tur |
| By: | Cirer-Costa, Joan Carles |
| Abstract: | This study aims to predict the possible negative effects on the Balearic tourism economy of the exploitation of marine oil fields near its coastline. We describe the current business structure of the islands’ tourism industry and then focus on the various kinds of spills that might affect it. Our conclusion is that the exploitation of the oil fields will significantly damage the tourism industry: a series of small-scale accidents followed by a large spill could destroy the complex structure of Balearic tourism, and would have severe repercussions for the economy of the archipelago. |
| Keywords: | Oil spill; tourism; Balearics |
| JEL: | H41 H84 K32 Q34 Q52 |
| Date: | 2015–01–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:61164&r=tur |
| By: | Oscar Claveria (Department of Econometrics. University of Barcelona ); Enric Monte (Department of Signal Theory and Communications. Polytechnic University of Catalunya. ); Salvador Torra (Department of Econometrics & Riskcenter-IREA. Universitat de 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:aqr:wpaper:201503&r=tur |