This paper improves short-term forecasting models of monthly tourism arrivals by estimating and evaluating a time-series model with exogenous regressors (ARIMA-X) using a case of Aruba, a small open tourism-dependent economy. Given importance of the US market for Aruba, it investigates informational value of Google Searches originating in the USA, flight capacity utilization on the US air-carriers, and per capita demand of the US consumers, given the volatility index in stock markets (VIX). It yields several insights. First, flight capacity is the best variable to account for the travel restrictions during the pandemic. Second, US real personal consumption expenditure becomes a more significnat predictor than income as the former better captured impact of the COVID-19 restrictions on the consumers’ behavior, while income boosted by the pandemic fiscal support was not fully directed to spending. Third, intercept correction improves the model in the estimation period. Finally, the pandemic changed econometric relationships between the tourism arrivals and their main determinants, and accuracy of the forecast models. Going forward, the analysts should re-estimate the models. Out-of-sample forecasts with 5 percent confidence intervals are produced for 18 months ahead.

This departmental paper analyzes the impact of the COVID-19 pandemic on tourism in the Asia Pacific region, Latin America, and Caribbean countries. Many tourism dependent economies in these regions, including small states in the Pacific and the Caribbean, entered the pandemic with limited fiscal space, inadequate external buffers, and foreign exchange revenues extremely concentrated in tourism. The empirical analysis leverages on an augmented gravity model to draw lessons from past epidemics and finds that the impact of infectious diseases on tourism flows is much greater in developing countries than in advanced economies.

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.

A textbook for a graduate or final-year undergraduate course in tourism studies that might also find interest among researchers and practitioners who want to apply recent developments in econometric modeling and forecasting to tourism demand analysis. Song and Witt (both management in the service sector, U. of Surrey, Britain) begin with the fundamentals of tourism demand analysis, and the problems of traditional modeling and forecasting. Then they explore the general- to-specific approach, the time-varying parameter model, and the panel- data approach. Annotation copyrighted by Book News, Inc., Portland, OR

'Forecasting tourism demand' is a text that no tourism professional can afford to be without. The tourism industry has experienced an overwhelming boom over recent years, and being able to predict future trends as accurately as possible is vital in the struggle to stay one step ahead of the competition. Building on the success of 'Practical Tourism Forecasting' this text looks at 13 methods of forecasting and with a user friendly style, 'Forecasting Tourism Demand' guides the reader through each method, highlighting its strengths and weaknesses and explaining how it can be applied to the tourism industry. 'Forecasting Tourism Demand' employs charts and tables to explain how to: * plan a forecasting project * analyse time series and other information * select the appropriate forecasting model * use the model for forecasting and evaluate its results Ideal for marketing managers and strategic planners in business, transportation planners and economic policy makers in government who must project demand for their products among tourists. Executives who rely on forecasts prepared by others will find it invaluable in assisting them to evaluate the validity and reliability of predictions and forecasts. Those engaged in analysing business trends will find it useful in surveying the future of what has been called the largest industry in the world.

After a period of rapid economic growth associated with high commodity prices, the region had entered a phase of lackluster performance. Recent developments, including a new oil price shock, and the outbreak of the Covid-19 epidemic will push the region into recession. Many countries are struggling to contain the spread of the Covid-19 epidemic while avoiding a dramatic decline in economic activity. The report analyzes how to think about this tradeoff. It estimates the potential health costs, assesses the effectiveness of diverse containment strategies, and discusses how large the economic cost could be. The current crisis is unprecedented because it combines a fall in global demand, tighter financial conditions and a major supply shock. The response needs to consider how to socialize the losses, how to prevent a collapse of the financial sector, how to protect jobs and livelihoods, and how to manage and divest the assets that will inevitably end up in the hands of the state.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.