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Econometric Modelling and Forecasting of Tourism Demand

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Absolute Scaled Error
ADLM
advanced statistical methods
Age Group_Uncategorized
Age Group_Uncategorized
Ante Forecasts
automatic-update
B01=Doris Chenguang Wu
B01=Gang Li
B01=Haiyan Song
BPNN Model
Category1=Non-Fiction
Category=KCH
Category=KNP
Category=KNSG
Category=S
Category=WS
cointegration techniques
Combining Forecasts
COP=United Kingdom
crisis forecasting tourism demand
Delivery_Delivery within 10-20 working days
Density Forecasts
eq_bestseller
eq_business-finance-law
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_sports-fitness
Forecast Tourist Arrivals
Forecasting Horizons
Format=BB
Format_Hardback
Global Vector Autoregressive Models
GVAR
International Monetary Fund
Judgemental Forecasting
Language_English
Mainland China
MIDAS
MIDAS Regression
Mixed Frequency Data
PA=Available
Price_€100 and above
PS=Active
R programming applications
Relative Tourism Price
SARIMA
scenario analysis tourism
softlaunch
Spatial Lag
spatiotemporal analysis
time series modelling
Tourism Demand
Tourism Demand Elasticities
Tourism Demand Forecasting
Tourism Demand Literature
TVP Model
Var Model

Product details

  • ISBN 9781032216423
  • Format: Hardback
  • Weight: 358g
  • Dimensions: 152 x 229mm
  • Publication Date: 27 Oct 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
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Description

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.
About

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.