Tourism income and economic growth in Greece: Empirical evidence from their cyclical components

This paper examines the relationship between the cyclical components of Greek GDP and international tourism income for Greece for the period 1976–2004. Using spectral analysis the authors find that cyclical fluctuations of GDP have a length of about nine years and that international tourism income has a cycle of about seven years. The volatility of tourism income is more than eight times the volatility of the Greek GDP cycle. VAR analysis shows that the cyclical component of tourism income is significantly influencing the cyclical component of GDP in Greece. The findings support the tourism-led economic growth hypothesis and are of particular interest and importance to policy makers, financial analysts and investors dealing with the Greek tourism industry

The Nature of the Relationship between International Tourism and International Trade: The Case of Ge

This paper deals with the relationship between international trade and tourism. In particular, we focus on the effect that German tourism to Spain has on German imports of Spanish wine. Due to the different stochastic properties of the series under analysis, which display different orders of integration, we use a methodology based on long memory regression models, where tourism is supposed to be exogenous. The results show that at the aggregate level, tourism has an effect on wine imports that lasts between two and nine months. Disaggregating the imports across the different types of wine it is observed that only for red wines from Navarra, Penedús and Valdepeñas, and to a certain extent for sparkling wine, tourism produces an effect on its future demand. From a policy-making perspective our results imply that the impact of tourism on the host economy is not only direct and short-term but also oblique and delayed, thus reinforcing the case for tourism as a means for economic development.

Tourism demand modelling and forecasting : a review of recent research

2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics

In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon

Tourism in the Canary Islands: Forecasting Using Several Seasonal Time Series Models

This paper deals with the analysis of the number of tourists travelling to the Canary Islands by means of using different seasonal statistical models. Deterministic and stochastic seasonality is considered. For the latter case, we employ seasonal unit roots and seasonally fractionally integrated models. As a final approach, we also employ a model with possibly different orders of integration at zero and the seasonal frequencies. All these models are compared in terms of their forecasting ability in an out-of-sample experiment. The results in the paper show that a simple deterministic model with seasonal dummy variables and AR(1) disturbances produce better results than other approaches based on seasonal fractional and integer differentiation over short horizons. However, increasing the time horizon, the results cannot distinguish between the model based on seasonal dummies and another using fractional integration at zero and the seasonal frequencies.

A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics

Working paperIn this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon.Preprin

Signal Extraction and Forecasting of the UK Tourism Income Time Series. A Singular Spectrum Analysis Approach

We present and apply the Singular Spectrum Analysis (SSA), a relatively new, non-parametric and data-driven method used for signal extraction (trends, seasonal and business cycle components) and forecasting of the UK tourism income. Our results show that SSA outperforms slightly SARIMA and time-varying parameter State Space Models in terms of RMSE, MAE and MAPE forecasting criteria.Singular Spectrum Analysis; Singular Value Decomposition; Business Cycle Decomposition; Tourism Income; United Kingdom; Signal Extraction; Forecasting

UK tourism arrivals and departures: seasonality, persistence and time trends

Issues such as seasonality, persistence and trends are examined in the series referring to the number of UK arrivals and departures using techniques based on fractional integration. This methodology is much more flexible than others based on integer degrees of differentiation and permits us to describe in a more general way the effects of shocks in the series. Our results indicate that the series display significant time trends; they show high persistence with orders of integration in the fractional range, thus showing long-lasting effects of shocks; seasonality is an important issue, and in removing the seasonality through seasonal differentiation, the time trends disappear though persistence remains as a relevant feature of the data. Policy implications of the results obtained are displayed at the end of the article