Pedro Gouveia

Dating and synchronizing tourism growth cycles.

The authors use the non-parametric method proposed by Harding and Pagan (2003) to date tourism growth cycles. This study is among the first to use robust, transparent and replicable dating rules in the context of economic tourism activity cycles. On the basis of a cycle indicator function, the authors are able to establish a greater degree of cycle synchronization of tourism demand than that observed at the economic cycle level, and, by means of a recursive correlation coefficient, they conclude that this degree of cycle synchronization has increased over the years. To analyse the presence of a time lag between turning points of economic cycles and tourism demand, they suggest a lag concordance index. Observing cycles and producing dating indicator functions are important in examining potential asymmetric behaviour associated with tourism economic phases and are useful for forecasting purposes.

Threshold Cointegration and the PPP Hypothesis

Self-Exciting Threshold Autoregressive (SETAR) models are a non-linear variant of conventional linear Autoregressive (AR) models. One advantage of SETAR models over conventional AR models lies in its flexible nature in dealing with possible asymmetric behaviour of economic variables. The concept of threshold cointegration implies that the Error Correction Mechanism (ECM) at a particular interval is inactive as a result of adjustment costs, and active when deviations from equilibrium exceed certain thresholds. For instance, the presence of adjustment costs can, in many circumstances, justify the fact that economic agents intervene to recalibrate back to a tolerable limit, as in the case when the benefits of adjustment are superior to its costs. We introduce an approach that accounts for potential asymmetry and we investigate the presence of the relative version of the purchasing power parity (PPP) hypothesis for 14 countries. Based on a threshold cointegration adaptation of the unit root test procedure suggested by Caner & Hansen (2001), we find evidence of an asymmetric adjustment for the relative version of PPP for eight pairs of countries.

An application of PAR models for tourism forecasting.

This paper extends the existing literature on tourism forecasting by developing an application study based on periodic models. The need for this type of approach derives from the common procedure in tourism literature of classifying series into three seasons: peak, shoulder and off-peak. This classification is useful in identifying more parsimonious periodic models when compared with unrestricted periodic monthly models. The authors apply their proposed models on several tourism series collected and analysed from Portugals southernmost province, Algarve. The economy of this region relies heavily on the tourism industry, catering largely for the European market. Besides statistically validating these models, the authors compare their forecasting ability with other models in the current literature. The results show that the models presented achieve a superior forecasting performance.

Modelling and Forecasting the UK Tourism Growth Cycle in Algarve

Over the past three decades, Portugal has developed a strong economic dependence on tourism, which has several implications for the countrys overall economic development. Tourism is an activity that is interrelated strongly with the economic system since Portugal as a whole and specific regions in particular rely on the performance of tourism for their economic activity. Moreover, because economic cycles affect tourism development, it is highly vulnerable to economic fluctuations. Most tourists who visit Portugal are from the European Union, especially Western Europe. Statistics are based on the number of overnight stays in hotel accommodation and other similar establishments. In 2005, the main source markets were the UK (30.7%), Germany (16.5%), Spain (11.5%), the Netherlands (6.8%), France (4.7%), Ireland (3.6%) and Italy (3.1%). These values show that the UK has the greatest share of visitors to Algarve. The purpose of this paper is to propose a modelling approach that best fits the tourism flow pattern in order to support forecasting. The paper contributes to our understanding of the relationship between economic cycles and tourism flows to Portugal (Algarve) and explores the potential of applying the diffusion index model proposed by Stock and Watson (1999, 2002) for tourism demand forecasting.

A note on seasonal breaks in tourism demand time series

In this paper, a structural break test procedure proposed in literature is employed in a tourism demand analysis context. This article is the first to apply a graphical approach using seasonal surface and seasonal contour maps, which can be important tools for seasonal pattern evolution analyses. For the purpose of empirical study, total lodging figures were selected to analyze British tourism demand in the Algarve. The seasonality of tourism demand has several effects on economic activity. Identifying seasonal breaks in tourism time series allowed us to obtain evidence of efforts to lessen seasonality and that can hopefully benefit long-run policy evaluation and planning.