Modelling and Simulation: An Overview

The papers in this special issue of Mathematics and Computers in Simulation cover the following topics: improving judgmental adjustment of model-based forecasts, whether forecast updates are progressive, on a constrained mixture vector autoregressive model, whether all estimators are born equal: the empirical properties of some estimators of long memory, characterising trader manipulation in a limit-order driven market, measuring bias in a term-structure model of commodity prices through the comparison of simultaneous and sequential estimation, modelling tail credit risk using transition matrices, evaluation of the DPC-based inclusive payment system in Japan for cataract operations by a new model, the matching of lead underwriters and issuing firms in the Japanese corporate bond market, stochastic life table forecasting: a time-simultaneous fan chart application, adaptive survey designs for sampling rare and clustered populations, income distribution inequality, globalization, and innovation: a general equilibrium simulation, whether exchange rates affect consumer prices: a comparative analysis for Australia, China and India, the impacts of exchange rates on Australia's domestic and outbound travel markets, clean development mechanism in China: regional distribution and prospects, design and implementation of a Web-based groundwater data management system, the impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence, and coercive journal self citations, impact factor, journal influence and article influence

Modelling and Simulation: An Overview

The papers in this special issue of Mathematics and Computers in Simulation cover the following topics: improving judgmental adjustment of model-based forecasts, whether forecast updates are progressive, on a constrained mixture vector autoregressive model, whether all estimators are born equal: the empirical properties of some estimators of long memory, characterising trader manipulation in a limit-order driven market, measuring bias in a term-structure model of commodity prices through the comparison of simultaneous and sequential estimation, modelling tail credit risk using transition matrices, evaluation of the DPC-based inclusive payment system in Japan for cataract operations by a new model, the matching of lead underwriters and issuing firms in the Japanese corporate bond market, stochastic life table forecasting: a time-simultaneous fan chart application, adaptive survey designs for sampling rare and clustered populations, income distribution inequality, globalization, and innovation: a general equilibrium simulation, whether exchange rates affect consumer prices: a comparative analysis for Australia, China and India, the impacts of exchange rates on Australia's domestic and outbound travel markets, clean development mechanism in China: regional distribution and prospects, design and implementation of a Web-based groundwater data management system, the impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence, and coercive journal self citations, impact factor, journal influence and article influence

Exchange Rate Exposure: Does exchange rate movement influence tourism development?

This paper examines the impact of exchange rate exposure on tourism demand using a dynamic panel of 23 sub Saharan Africa’s tourist destinations. Although, the research question for the paper focuses on whether uncertainty on the exchange rate can help explain why could exchange rate fluctuation co-move with the travel expenditure using data from these selected African tourist destinations as well as the variations across countries in recent years. Utilising annual data from 1996 to 2015 on dynamic panel estimation techniques, we provide evidence which suggests that both variables exchange rate fluctuation and travel expenditure are statistically significant determinants of tourism demand. The Penal autoregressive distributed lagged ARDL panel cointegration test is utilised to examine the existence of a long run association between exchange rate and travel expenditure and the findings from the panel cointegration test reveals that real income, real xchange rates, price inflation and travel expenditure and international tourist arrival have long run relationship. We also employed pool (OLS), fixed effect (FE) and random effect (RE) models to investigate which of the models in questions can at most have useful information to explain tourism demand subject to travel expenditure with respect to the selected sampled tourist destinations in Africa

Better understanding of exchange rate effects in destination marketing: Cases of the Czech Republic and Croatia

Exchange rates can be considered as one of the important determinants in tourism demand analysis especially at the national level, although sensitivity of demand to exchange rate changes may also vary by destination. The main goal of this paper is to compare the impact of changes in nominal exchange rates on the number of visitors from Eurozone countries in two specific destinations: Croatia, representing a Southern Europe/Mediterranean, predominantly \u27sun and sea\u27 destination and the Czech Republic, as a Central and Eastern European country and a typical cultural destination. A generalized linear model was used for data analysis and hypothesis testing. This paper identifies relationship between seasonally cleaned changes in the number of incoming tourists from Eurozone and average changes in monthly nominal exchange rate lagged between one and twelve months. The results show that there are significant differences in sensitivity of international tourism demand from Eurozone to changes in exchange rate between Czech Republic and Croatia and that delays in reaction to such changes are as expected different as well. The findings could improve short term forecasting of tourism demand as well as marketing targeting of destination management organization (DMO) activities. The results of this study can be practically used by National Tourism Organizations (NTOs) for marketing activities, because it is possible to reveal the tourists\u27 behaviour from the source markets point of view. These findings, based on secondary data, could be used as a support tool for destination marketing, partially instead of primary data collection within the source markets

The phenomenon of pilgrimages to Marian Sanctuaries

By centuries pilgrimages are present in Christianity. For Catholics, the importance of devotions and visits to the Marian sanctuaries is indisputable. The number of visitors and pilgrims to these temples makes the local economy an important destination for religious tourism. In order to understand the economic determinants of religious tourism, two sanctuaries were studied, namely, Aparecida (Brazil) and Fatima (Portugal). Given the large collection of statistical information of the Portuguese Sanctuary, it was verified through the Vector Autoregressive model that Gross Domestic Product and Unemployment cause unidirectional the pilgrimages. The Autoregressive Distributed Lag model revealed that the increase in Gross Domestic Product and international arrivals in the short term positively impacts the number of pilgrims. Through the Ordinary Least Squares regression, significant statistical relationships between climatic factors (rain volume and average temperature) and visitors in the Sanctuary of Fatima were found. The Seasonal Autoregressive Integrated Moving Average forecast method was applied to the number of monthly visitors to the Sanctuary of Aparecida and to the number of pilgrims in the Sanctuary of Fatima, the results show a strong seasonality and that the first and last months of the year are periods of low demand. The results of this study allow a new look at religious tourism in the Marian context, the empirical results allow those responsible for establishing public policies, tourism agents and the administration of the Sanctuaries to direct yours actions. Measures planned and executed jointly between the various agents can benefit residents, visitors, pilgrims, the tourism sector and the Sanctuaries themselves.Há séculos as peregrinações estão presentes no cristianismo. Para os católicos é indiscutível a importância das devoções e visitações aos Santuários marianos. O número de pessoas que visitam e peregrinam a esses espaços influenciam a economia local. Para compreeder os determinantes economicos do turismo religioso mariano foram estudados dois Santuários, nomeadamente, Aparecida (Brasil) e Fátima (Portugal). Dado o grande acervo de informações estatísticas do santuário português, verificou-se, através do modelo Vector Autoregressive, que o Produto Interno Bruto e o Desemprego influenciam unidirecionalmente as peregrinações. O modelo Autoregressive Distributed Lag revelou que o aumento do Produto Interno Bruto e das chegadas internacionais no curto prazo impactam positivamente o número de peregrinos. Por meio da regressão Ordinary Least Squares foram encontradas relações estatísticas significantes entre fatores climáticos (volume de chuva e temperatura média) e visitantes no Santuário de Fátima. O método de previsão Sazonal Autoregressive Integrated Moving Average foi aplicado nas séries (i) número de visitantes mensais do Santuário de Aparecida; e (ii) número de peregrinos no Santuário de Fátima. Os resultados revelam uma forte sazonalidade e que os primeiros e últimos meses do ano são os períodos de baixa procura. Os resultados deste estudo permitem um novo olhar para o turismo religioso no contexto mariano, os resultados empíricos permitem que os responsáveis por estabelecerem políticas públicas, agentes do turismo e a administração dos Santuários direcionem suas ações. Ações planejadas e executadas em conjunto entre os diversos agentes podem beneficiar os residentes, visitantes, peregrinos, o setor do turismo e os próprios Santuários

Volatility of city tourism demand

The main objectives of this research are to identify, through a systematic literature review, the potential benefits of the use of volatility models in tourism, to study the volatility of tourism demand in cities and to compare models of volatility between different destinations and source markets. The three cities analysed in Portugal were Coimbra, Lisbon and Oporto and the source markets that were studied were the domestic market, the total overnight stays, Brazil, France, Germany, Italy, Spain, the United Kingdom and other non-specified countries. The systematic review of the literature was carried out in order to identify, in a temporal perspective, the use of each methodology, variables used, data frequencies, temporal window, type of territories and geographic object of each study. The semantic analysis of the state of the art was also a methodology used. After a preliminary analysis of the time series, models that literature indicates as more suitable to estimate the volatility were used, namely, models of autoregressive conditional heteroscedasticity: ARCH, GARCH, EGARCH and TGARCH models. The most suitable models for each source market, in each city, were identified, as well as the existence of asymmetries face to positive and negative shocks, their magnitude and their persistence. Different models of volatility were identified in each city for each source market, as well as, different types of persistence of volatility, in each market and city, and different magnitude in face of good news and bad news, which strengthens the need to adjust the modelling of tourism demand for each market and, within a country, at a more detailed territorial scale. The use of volatility models is quite recent in tourism demand modelling and had not yet been applied in cities in Portugal, for which, despite the growing importance in terms of tourism, there are no studies of modelling focusing on the tourism demand. Modelling tourism demand is essential when tourism policymakers plan tourism activities. The tourism industry may be extremely sensitive to specific events’ effects, so good models must be found that reflect volatility that varies within each city and for each source market and policies must be adapted to each of the source/destination pairs.info:eu-repo/semantics/draf

Volatility in city tourism demand

The main objectives of this research are to identify, through a systematic literature review, the potential benefits of the use of volatility models in tourism, to study the volatility of tourism demand in cities and to compare models of volatility between different destinations and source markets. The three cities analysed in Portugal were Coimbra, Lisbon and Oporto and the source markets that were studied were the domestic market, the total overnight stays, Brazil, France, Germany, Italy, Spain, the United Kingdom and other non-specified countries. The systematic review of the literature was carried out in order to identify, in a temporal perspective, the use of each methodology, variables used, data frequencies, temporal window, type of territories and geographic object of each study. The semantic analysis of the state of the art was also a methodology used. After a preliminary analysis of the time series, models that literature indicates as more suitable to estimate the volatility were used, namely, models of autoregressive conditional heteroscedasticity: ARCH, GARCH, EGARCH and TGARCH models. The most suitable models for each source market, in each city, were identified, as well as the existence of asymmetries face to positive and negative shocks, their magnitude and their persistence. Different models of volatility were identified in each city for each source market, as well as, different types of persistence of volatility, in each market and city, and different magnitude in face of good news and bad news, which strengthens the need to adjust the modelling of tourism demand for each market and, within a country, at a more detailed territorial scale. The use of volatility models is quite recent in tourism demand modelling and had not yet been applied in cities in Portugal, for which, despite the growing importance in terms of tourism, there are no studies of modelling focusing on the tourism demand. Modelling tourism demand is essential when tourism policymakers plan tourism activities. The tourism industry may be extremely sensitive to specific events’ effects, so good models must be found that reflect volatility that varies within each city and for each source market and policies must be adapted to each of the source/destination pairs

SOCIAL SCIENCES POSTGRADUATE INTERNATIONAL SEMINAR (SSPIS) 2017

The objectives of this seminar are: To provide an avenue for postgraduate students to present their research findings, impart knowledge and get feedback; To promote interactions among participants; To enhance networking among researchers; and To assist postgraduate students with publication opportunities