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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.
Doris Chenguang Wu, Ph.D., is a Professor in the School of Business at Sun Yat-sen University, China. Her research interests include tourism demand forecasting and tourism big data analytics. Gang Li, Ph.D., is a Professor of Tourism Economics at the University of Surrey. His research interests include economic analysis and forecasting of tourism demand. Haiyan Song, Ph.D., is Chan Chak Fu Professor of International Tourism in the School of Hotel and Tourism Management at the Hong Kong Polytechnic University. His research interests are in tourism demand modelling and forecasting, tourism supply chain management, and wine economics.
1. Overview of Econometric Tourism Demand Modelling and Forecasting. 2. Theoretical Foundations, Key Concepts and Data Description. 3. The Autoregressive Distributed Lag Model. 4. The Time-Varying Parameter Model. 5. Vector Autoregressive Models. 6. Spatiotemporal Econometric Models. 7. Mixed-Frequency Models. 8. Hybrid Forecasting Models. 9. Density Forecasting. 10. Forecast Combinations. 11. Judgmental Forecasting. 12. Scenario Forecasting during Crises. 13. A Web-based Tourism Forecasting System. Epilogue.
