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The main goal of this study is to estimate the price and income elasticity of demand for tourism to Spain. This estimation is done separately for the major international source markets for Spain: Germany, the UK, Italy and the Netherlands. For this purpose, the authors use the autoregressive distributed lag (ARDL) approach to cointegration and the bootstrap method to construct empirical confidence intervals for each estimate. The results reveal that the tourism demand in all the countries studied has a similar income elasticity, which is approximately unitary. However, there is an important difference with regard to price elasticity: tourism demand from the UK is statistically price inelastic, but demand is elastic for the remaining countries. This finding is relevant because, first, it underlines the importance of studying the source markets separately instead of analysing an aggregate international tourism demand, and, second, it supports the need to implement different tourism policies and strategies with respect to the pricing decisions for each source market.

In this work we analyse unemployment duration for married women in Spain, using the Labour Force Survey (LFS) of the Spanish Institute for Statistics, 1987-1997. Consistent non-parametric estimation of the unemployment survival function is provided. Since the available data are length- biased, a suitable correction of the Kaplan-Meier product-limit estimator is motivated and used for the referred analysis. The accuracy of parametric models is checked by means of goodness-of-fit plots--a graphical tool that requires preliminary estimation of the survival. Structural features of the associated hazard (as monotonicity and unimodality) are explored.

This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.Artificial neural networks are mathematical models that are inspired by how biological neurons work and are organized. The purpose of this section is to present a brief overview of this computer-based modeling procedure. For a more detailed technical explanation about artificial neural networks, the reader is referred to the books by [31,32]; and for empirical applications, recommended reading includes [33,34]. In all these references, we can see that there are many different types of neural network models that can be used for forecasting purposes, such as radial basis or recurrent networks. The neural network that was used in our specific forecasting study is a feed-forward multi-layer network with a learning algorithm based on the back-propagation algorithm. This network is by far the most popular and successful to predict economic and financial series [35], as well as tourism data [36,37]. The main reason for its popularity is that many theoretical studies have shown that this network can reflect any non-linear dynamic with a certain level of accuracy [38,39]. In particular, we

It is widely argued that low-cost carriers (LCCs) lead to an increase in tourism demand. However, there is no conclusive evidence when the airport is located in a region with large diaspora and outbound tourism. To gain insight into the relationship between LCCs and international tourism demand, we analyse whether a causal relationship exists between the number of international LCC passengers at the Porto airport and international tourism demand in the Galicia-North Portugal Euroregion using a vector autoregressive model. We evaluate the dynamics of the impacts of the LCC passengers on international tourism demand in a tourism demand model framework. The number of low-cost international passengers has a positive influence on international tourism demand in the regions of North Portugal and Galicia (Spain). Furthermore, the confidence interval for estimated elasticity overlaps the values estimated for main tourism destinations in previous studies in the Iberian Peninsula.

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A large number of studies have been devoted to analyzing international tourism demand; however, even today, the impact of climate and weather on tourism receives only limited attention. We studied the empirical influence of the North Atlantic Oscillation (NAO), the most important mode of variability in northern atmospheric circulation, on international tourism demand -specifically, tourist arrivals from the UK and Germany to the Balearic archipelago (Spain). We used 2 traditional techniques usually applied in natural sciences, cross-correlation functions and the Granger causality test, as well as a novel and flexible methodology called the autoregressive distributed lag (ARDL) boundstesting approach. ARDL modeling can be a useful tool in illuminating relationships between variables. Our empirical evidence demonstrates the existence of a statistical relationship between the NAO and tourist arrivals from the UK and Germany to the Balearic Islands. The finding of a statistical relationship between the NAO and tourism suggests that this atmospheric phenomenon can be of great interest to social researchers who study international tourism flows. The NAO index can be used as an explanatory variable in tourism demand models, allowing researchers to develop parsimonious models, as well as to avoid certain economic problems (e.g. multicolinearity). KEY WORDS: North Atlantic Oscillation · International tourism demand · Granger causality test · Cross-correlation analysis · Autoregressive distributed lag · ARDL · Balearic IslandsResale or republication not permitted without written consent of the publisher 1 Climate and weather are meteorologically similar phenomena manifested and studied at different scales. De Freitas (2003) defines climate as the accumulation of daily and seasonal weather events over a long time period. On the other hand, weather is the condition of the atmosphere at a specific place and at a specific time Clim Res 43: 207-214, 2010 2007, Scott et al. 2008). The most widely used variables were temperature, rainfall, wet days, cloud cover, humidity, sunshine, and wind speed. However, the inclusion of these variables in a tourism-demand model could lead to some econometric problems since they are highly correlated to each other (multicollinearity problems). Other researchers have constructed indices that allow summarizing the significance of climate for tourism, but they were only used to evaluate and rate recreational climates in terms of user sensitivity and satisfaction (de Freitas 2003.One key variable that might be useful to summarize meteorological information and potentially useful to explain tourism is the recurrent pattern of atmospheric variability observed over the North Atlantic Ocean, known as the North Atlantic Oscillation (NAO). The phenomenon is formally defined as an anomalous difference in atmospheric pressure between a subtropical high-pressure belt (around the latitudes of 35 to 40°in the Northern Hemisphere and centered near the Azores) and a subpolar low-pressure belt (cen...

As an industry, tourism tends to be extremely responsive and vulnerable to political instabilities. Recently, a political conflict occurred in Spain, a leader in international tourism. In October 2017, the regional parliament of Catalonia asserted its independence from Spain, engendering a negative impact on the tourism sector of Catalonia. The main goal of our study is to assess the economic impact of the Catalan separatist challenge on the region's tourism sector during the last quarter of 2017. To this end, we conducted a counterfactual analysis, based on forecasts generated by a seasonal autoregressive moving average model and an artificial neural network. The forecasts allowed us to calculate the projected number of international and domestic tourist visitors that would have travelled to Catalonia, had the separatist challenge not occurred. According to our results, the Catalan tourist sector effectively forfeited close to €200 million in revenue from the international tourism market, and around €27 million in revenue from the domestic market. These amounts differ from the economic gains attained by the other Spanish Mediterranean regions that compete with Catalonia to attract tourists.