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This insightful and timely volume provides a succinct, expert-led introduction to the latest developments in advanced econometric methodologies in the context of tourism demand modelling and forecasting.Written by a plethora of worldwide experts on this topic, this book offers a comprehensive approach to tourism econometrics. Accurate demand forecasts are crucial to decision-making in the tourism industry and this book provides real-life tourism applications and the corresponding R code alongside theoretical foundations, in order to enhance understanding and practice amongst its readers. The methodologies introduced include general to specific modelling, cointegration, vector autoregression, time-varying parameter modelling, spatiotemporal econometric models, mixed-frequency forecasting, hybrid forecasting models, forecasting combination techniques, density forecasting, judgemental forecasting, scenario forecasting under crisis, and web-based tourism forecasting.Embellished with insightful figures and tables throughout, this book is an invaluable resource for those using advanced econometric methodologies in their studies and research, including both undergraduate and postgraduate students, researchers, and practitioners.

Fierce global competition in the tourism industry is now focused on integral parts of supply chains rather than on individual firms. The highly competitive environment has forced tourism firms to look for ways to enhance their competitive advantage. Tourism products are often viewed by consumers as a value-added chain of different service components and identifying ways to effectively manage the interrelated tourism business operations will enable tourism firms to better meet customer needs and accomplish business goals thus maintaining competitive advantage over their equally efficient rivals.This significant and timely volume is the first to apply supply chain management theories and practices in the context of tourism. By doing so the book offers insight into the relationships between tourism enterprises, how coordination across organizations can be effectively achieved and how business performance can be improved. It provides comprehensive and systematic coverage of modern supply chain management concepts and methodologies applied to the tourism and hospitality industries. The text covers key issues and principles including: marketing and product development, demand forecasting, supplier selection and management, distribution channels, capacity management, customer relationship management, tourism supply chain competition and coordination, and e-tourism. The book combines essential theory and comparative international examples based on primary research to show challenges and opportunities of effective tourism supply chain management. This text is essential for final year undergraduate and postgraduate students studying Tourism Management, Tourism Planning and Tourism Economics.

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest. Governments and companies such as airlines, tour operators, hotels, cruise ship lines, and recreation facility providers are interested in the demand for their products by tourists. The success of many businesses depends largely or totally on the state

Stay up to date with the most effective practices in tourism demand forecasting! Tourism Forecasting and Marketing presents vital, up-to-date research on the latest practice and applications of tourism demand modeling and forecasting. The book addresses both econometric and time series approaches to forecasting, focusing on the concepts, model specification, data analysis, and methodologies used in day-to-day tourism planning. An international panel of practitioners and academics call on a diverse range of empirical research findings to discuss commonly used theoretical frameworks for forecasting and future directions tourism demand is likely to take. Tourism Forecasting and Marketing presents research findings from the United States, the United Kingdom, Asia, and Australia that are invaluable for guiding government and private sector tourism investment and development decisions. The book addresses traditional versus modern forecasting techniques; evaluations of current and past forecasting methods; modeling and forecasting destination choice; and the impact of forecasting and marketing on tourism demand. Topics include: using time series models to forecast inbound tourism demand for China and Greece determining the economic factors that influence tourism demand in Hong Kong, Indonesia, and Malaysia examining domestic travel expenditures in South Korea developing a model to forecast ski tourism using the Palmore cohort analysis for tourism forecasting and much more! Tourism Forecasting and Marketing is an important textbook for educators and students working in tourism policy planning and management, and tourism marketing. The book is equally effective as a reference for travel and tourism researchers, and for professionals dealing with tourism demand analysis and forecasting.

The phenomenal growth of both the world-wide tourism industry and academic interest in tourism over the last thirty years has generated great interest in tourism demand modelling and forecasting from both sectors. However, the tendency for researchers and practitioners engaged in quantitative causal tourism modelling and forecasting to run many regression equations and try to choose the'best'model based on various parametric and non-parametric criteria has been widely criticised as failing to provide credible results. The aim of this book is to present the recent advances in econometric modelling methodology within the context of tourism demand analysis at a level that is accessible to non-specialists, and to illustrate these new developments with actual tourism applications. The book begins with an introduction to the fundamentals of tourism demand analysis, before addressing the problems of traditional tourism demand modelling and forecasting, i.e. data mining and spurious regression due to common trends in the time series. Three chapters explore the general-to-specific approach to tourism demand modelling and forecasting, including the use of autoregressive distributed lag processes, cointegration analysis and error correction models. The time varying parameter model together with the use of the Kalman filter as an estimation method is a useful tool for examining the effects of regime shifts on tourism demand elasticities: this is explored next. The panel data approach is introduced as a way of overcoming the problem of estimation and forecasting biases caused by insufficient time series data. The book concludes by evaluating the empirical forecasting performance of the various models and putting forward some general conclusions.