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on Tourism Economics |
| By: | Gabriela Mordecki (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Ana Leiva (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Nathalie Desplas (Tecnológico de Monterrey Campus Chihuahua (México).) |
| Abstract: | Tourism is frequently viewed as an important engine for the economic growth and country’s development. In Mexico, the domestic trips have become a notable feature but the main tourism exports are from internationals travelers. In 2014 Mexico was the 10th most attractive country for travelers and 58.3% came from the United States.For Uruguay, total yearly tourists represent about 90% of its population, and Argentinean tourists represent nearly 60% of this total and historically they have been themain visitors. Tourist activities have a great impact on both economies, and in this paper, we try to measure tourism demand comparing Mexico and Uruguay, two very different countries, but for both tourism is an important industry, generating employment and income. Therefore, it is central to analyze the determinants behind tourism demand. We study the relationship between the number of US tourists for Mexico and Argentinean tourists for Uruguay, analyzing the relationship with the income and the real exchange rate (RER) of each country. We studied long-run cointegration vectors between variables, following Johansen methodology. We found one cointegration relationship for each country, through Vector error correction models (VECM). We found an income-elasticity greater than 2 for American tourists in Mexico, and near 3 for Argentinean tourists in Uruguay. Bilateral RER also were significant in both models. |
| Keywords: | tourism demand, cointegration, real exchange rate |
| JEL: | C32 F14 F41 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:ulr:wpaper:dt-09-16&r=tur |
| By: | Anastasios Zopiatis (Department of Hotel and Tourism Management School of Business and Economics Cyprus University of Technology.); Christos S. Savva; Neophytos Lambertides; Michael McAleer |
| Abstract: | Following the recent terrorist attacks in Paris, the European media emphatically pronounced that billions of euros were wiped from tourism related stocks. This comes at a troublesome time for the tourism industry, in the midst of a global financial crisis, and the unpredictable rise of radical Islamic ideologies, which have caused chaos in the Middle East and Europe. The relationship and vulnerability of the industry to non-macro incidents have been well documented in the literature, mostly in theoretical terms. Nevertheless, the quantifiable impact of such events on tourism-specific stock values, both in terms of returns and volatility, received much less attention. With the use of an econometric methodology, the paper aims to enhance our conceptual capital pertaining to the effects of such possibilities on five hospitality and tourism stock indices. The empirical findings are of interest to stakeholders at all echelons of the spectra of the tourism and financial industries. |
| Keywords: | Tourism, Terrorism, Stock market, Event study, GJR, Econometric modeling. |
| JEL: | C21 C58 G01 H12 Z32 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1618&r=tur |
| By: | Oscar Claveria (AQR-IREA, University of Barcelona); Enric Monte (Polytechnic University of Catalunya (UPC)); Salvador Torra (Riskcenter-IREA, University of Barcelona) |
| Abstract: | This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation. |
| Keywords: | Regional forecasting, tourism demand, multiple-input multiple-output (MIMO), Gaussian process regression, neural networks, machine learning JEL classification: - |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:aqr:wpaper:201701&r=tur |