Essays on applied econometrics : applications of the futures markets and the international tourism demand

2008 ◽  
Author(s):  
Sang Young Jei
Keyword(s):  
Futures Markets ◽  
Applied Econometrics ◽  
International Tourism ◽  
Tourism Demand

Guidelines for Constructing a Tourist Price Index (English version)

1985 ◽  
Keyword(s):  
Price Index ◽  
International Tourism ◽  
Basic Information ◽  
Tourism Demand ◽  
English Version ◽  
Tourism Product

The purpose of these guidelines is to furnish National Tourism Administrations with basic information, assessments and guidelines for constructing a tourist price index: an instrument that makes it possible to guide tourism policies and define actions and measures to be taken with regard to prices and promotion of the tourism product in the light of evolution in the domestic and international tourism demand.

Estimating the Impact of Avian Flu on International Tourism Demand Using Panel Data

2009 ◽  
Vol 15 (3) ◽  
pp. 501-511 ◽  
Author(s):  
Hsiao-I Kuo ◽  
Chia-Lin Chang ◽  
Bing-Wen Huang ◽  
Chi-Chung Chen ◽  
Michael McAleer
Keyword(s):  
Panel Data ◽  
Pandemic Influenza ◽  
Fixed Effects ◽  
Data Models ◽  
International Tourism ◽  
Panel Data Models ◽  
Tourism Demand ◽  
Avian Flu ◽  
Potential Risks ◽  
The Impact

This paper investigates the impacts of avian flu on global and Asian tourism using panel data procedures. Both static and dynamic fixed effects panel data models are adopted to estimate the impacts of this infectious disease. The empirical results from static and dynamic fixed effects panel data models are consistent and indicate that the number of affected poultry outbreaks has significant impacts on the international tourism of global and Asian affected countries. The high mortality rate among humans, the potential of a global flu pandemic and some media frenzy with hype and speculation might adversely affect the images of these infected destinations as a safe tourist destination. Moreover, it was found that the average damage to Asian tourism was more serious, which might have been induced by an ineffective suppression in numerous Asian infected countries. In addition, Asia was the earliest affected region and the area infected most seriously by avian flu, both in humans and in poultry. Since the potential risks and damage arising from avian flu and the subsequent pandemic influenza are much greater than for previous diseases, the need to take necessary precautions in the event of an outbreak of avian flu and pandemic influenza warrants further attention and action in modelling and managing international tourism demand and risk.

Forecasting international tourism demand: a local spatiotemporal model

2020 ◽  
Vol 83 ◽  
pp. 102937
Author(s):  
Xiaoying Jiao ◽  
Gang Li ◽  
Jason Li Chen
Keyword(s):  
International Tourism ◽  
Tourism Demand ◽  
Spatiotemporal Model

scholarly journals Tourism under the Early Phase of COVID-19 in Four APEC Economies: An Estimation with Special Focus on SARS Experiences

2020 ◽  
Vol 17 (20) ◽  
pp. 7543
Author(s):  
Bao-Linh Tran ◽  
Chi-Chung Chen ◽  
Wei-Chun Tseng ◽  
Shu-Yi Liao
Keyword(s):  
Hong Kong ◽  
New Zealand ◽  
Fixed Effects ◽  
Negative Impact ◽  
Asia Pacific ◽  
International Tourism ◽  
Special Focus ◽  
Tourism Demand ◽  
Data Set ◽  
The Impact

This study examines how experience of severe acute respiratory syndrome (SARS) influences the impact of coronavirus disease (COVID-19) on international tourism demand for four Asia-Pacific Economic Cooperation (APEC) economies, Taiwan, Hong Kong, Thailand, and New Zealand, over the 1 January–30 April 2020 period. To proceed, panel regression models are first applied with a time-lag effect to estimate the general effects of COVID-19 on daily tourist arrivals. In turn, the data set is decomposed into two nation groups and fixed effects models are employed for addressing the comparison of the pandemic-tourism relationship between economies with and without experiences of the SARS epidemic. Specifically, Taiwan and Hong Kong are grouped as economies with SARS experiences, while Thailand and New Zealand are grouped as countries without experiences of SARS. The estimation result indicates that the number of confirmed COVID-19 cases has a significant negative impact on tourism demand, in which a 1% COVID-19 case increase causes a 0.075% decline in tourist arrivals, which is a decline of approximately 110 arrivals for every additional person infected by the coronavirus. The negative impact of COVID-19 on tourist arrivals for Thailand and New Zealand is found much stronger than for Taiwan and Hong Kong. In particular, the number of tourist arrivals to Taiwan and Hong Kong decreased by 0.034% in response to a 1% increase in COVID-19 confirmed cases, while in Thailand and New Zealand, a 1% national confirmed cases increase caused a 0.103% reduction in tourism demand. Moreover, the effect of the number of domestic cases on international tourism is found lower than the effect caused by global COVID-19 mortality for the economies with SARS experiences. In contrast, tourist arrivals are majorly affected by the number of confirmed COVID-19 cases in Thailand and New Zealand. Finally, travel restriction in all cases is found to be the most influencing factor for the number of tourist arrivals. Besides contributing to the existing literature focusing on the knowledge regarding the nexus between tourism and COVID-19, the paper’s findings also highlight the importance of risk perception and the need of transmission prevention and control of the epidemic for the tourism sector.

scholarly journals Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

2018 ◽  
Vol 58 (3) ◽  
pp. 383-397 ◽  
Author(s):  
A. George Assaf ◽  
Gang Li ◽  
Haiyan Song ◽  
Mike G. Tsionas
Keyword(s):  
Regional Integration ◽  
Spillover Effects ◽  
International Tourism ◽  
Tourism Demand ◽  
Vector Autoregressive ◽  
Regional Tourism ◽  
International Tourist ◽  
Tourist Flows ◽  
Modeling And Forecasting ◽  
Global Vector

Increasing levels of global and regional integration have led to tourist flows between countries becoming closely linked. These links should be considered when modeling and forecasting international tourism demand within a region. This study introduces a comprehensive and accurate systematic approach to tourism demand analysis, based on a Bayesian global vector autoregressive (BGVAR) model. An empirical study of international tourist flows in nine countries in Southeast Asia demonstrates the ability of the BGVAR model to capture the spillover effects of international tourism demand in this region. The study provides clear evidence that the BGVAR model consistently outperforms three other alternative VAR model versions throughout one- to four-quarters-ahead forecasting horizons. The potential of the BGVAR model in future applications is demonstrated by its superiority in both modeling and forecasting tourism demand.

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