Dataset Information

Modelling the volatility of international visitor arrivals to New Zealand.

ABSTRACT: International visitor arrivals are considered to be a major source of foreign exchange, tourism-related employment and other tourism-related activities. This study used SARIMAX/(E)GARCH volatility models to forecast visitor arrivals by air transport to New Zealand from its eight key tourist source markets (Australia, Canada, China, Japan, South Korea, Germany, the United Kingdom (UK) and the United States (US)) and control macroeconomic factors together with global and regional structural changes. The empirical models reveal that the macroeconomic factors contributed at various levels to different markets, and the models we provided made accurate and reliable forecasts for visitor arrivals by air transport from all studied markets. The results from the markets for Germany, Japan, South Korea and the UK showed that significantly negative tourism demand shocks increased the volatility of tourism arrivals, more than positive tourism demand shocks of equal magnitude. Accordingly, the findings of this study will allow policy-makers in the New Zealand tourism sector and other stakeholders (e.g. airline management) to better understand the impacts on the volatility of visitor arrivals to New Zealand.

SUBMITTER: Balli HO

PROVIDER: S-EPMC7148906 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Modelling the volatility of international visitor arrivals to New Zealand.

Balli Hatice Ozer HO Tsui Wai Hong Kan WHK Balli Faruk F

Journal of air transport management 20181123

International visitor arrivals are considered to be a major source of foreign exchange, tourism-related employment and other tourism-related activities. This study used SARIMAX/(E)GARCH volatility models to forecast visitor arrivals by air transport to New Zealand from its eight key tourist source markets (Australia, Canada, China, Japan, South Korea, Germany, the United Kingdom (UK) and the United States (US)) and control macroeconomic factors together with global and regional structural change ...[more]

PMID: 32834689

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Project description:BackgroundAccident and Emergency Department (AED) is the frontline of providing emergency care in a hospital and research focusing on improving decision-makings and service level around AED has been driving a rising number of attentions in recent years. A retrospective review among the published papers shows that related research can be classified according to six planning modules: demand forecasting, days-off scheduling, shift scheduling, line-of-work construction, task assignment and staff assignment. As patient arrivals demand forecasts enable smooth AED operational planning and help decision-making, this article conducted a systematic review on the statistical modelling approaches aimed at predicting the volume of AED patients' arrival.MethodsWe carried out a systematic review of AED patient arrivals prediction studies from 2004 to 2021. The Medline, ScienceDirect, and Scopus databases were searched. A two-step screening process was carried out based on the title and abstract or full text, and 35 of 1,677 articles were selected. Our methods and results follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We categorise AED methods for modelling patient arrivals into four main classes: regression, time series, artificial intelligence and time series regression. Choice of prediction model, selection of factors and model performance are compared. Finally, we discuss the advantages and limitations of the models and suggest future research directions.ResultsA total of 1,677 papers that fulfilled the initial searching criteria was obtained from the three databases. Based on the first exclusion criteria, 1,603 articles were eliminated. The remaining 74 full text articles were evaluated based on the second exclusion criteria. Finally, 35 articles were selected for full review. We find that the use of artificial intelligence-based model has risen in recent years, from the view of predictive model selection. The calendar-based factors are most commonly used compared with other types of dependent variables, from the view of dependent variable selection.ConclusionsAll AEDs are inherently different and different covariables may have different effects on patient arrivals. Certain factors may play a key role in one AED but not others. Based on results of meta-analysis, when modelling patient arrivals, it is essential to understand the actual AED situation and carefully select relevant dominating factors and the most suitable modelling method. Local calibration is also important to ensure good estimates.

Dataset Information

Modelling the volatility of international visitor arrivals to New Zealand.

Publications

Modelling the volatility of international visitor arrivals to New Zealand.

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