Advanced Econometrics of Tourism Demand

Stephen F Witt is a Visiting Professor in the School of Hotel and Tourism Management at The Hong Kong Polytechnic University and is an Emeritus Professor at the University of Surrey, UK. Prior to this he was Professor of Tourism Forecasting in the School of Management at the University of Surrey, UK. He has published 150 journal articles and book chapters, as well as 20 books. The latter include: the Tourism Marketing and Management Handbook ; The Management of International Tourism ; Modeling and Forecasting Demand in Tourism ; Tourism Demand Modelling and Forecasting: Modern Econometric Approaches ; Tourism Forecasts for Europe 2001-2005 ; and Asia Pacific Tourism Forecasts 2006 -2008 . Gang Li obtained his BA and MSc degrees from the Dongbei University of Finance and Economics (DUFE) in China and PhD from the University of Surrey in England. His research interests include tourism economics, statistics and Chinese economy. He has published a number of articles in such academic journals as: International Journal of Forecasting , Journal of Travel Research , Tourism Management , and Tourism Economics . Haiyan Song has a background in Economics and his main research area is tourism economics with a particular focus on tourism demand modeling and forecasting. He was educated both in China and the UK and has extensive research experiences in such areas as foreign direct investment (FDI) in China and economic issues related to China's tourism sector. Song joined the Hong Kong Polytechnic University as Chair Professor in Tourism in 2004 and before that he was Professor of Economics (Tourism) at the University of Surrey in the UK.

• 1. Introduction to Tourism Demand Analysis 2. Recent Developments in Tourism Demand Analysis 3. Traditional Methodology of Tourism Demand Modelling 4. General-to-Specific Modelling 5. Cointegration 6. Error Correction Model 7. Vector Autoregression (VAR) and Cointegration 8. Time Varying Parameter Modelling 9. Panel Data Analysis 10. Systems of Demand Equations 11. Evaluation of Forecasting Performance