Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.

As the pandemic heigthened policymakers’ demand for more frequent and timely indicators to assess economic activities, traditional data collection and compilation methods to produce official indicators are falling short—triggering stronger interest in real time data to provide early signals of turning points in economic activity. In this paper, we examine how data extracted from the Google Places API and Google Trends can be used to develop high frequency indicators aligned to the statistical concepts, classifications, and definitions used in producing official measures. The approach is illustrated by use of Google data-derived indicators that predict well the GDP trajectories of selected countries during the early stage of COVID-19. To this end, we developed a methodological toolkit for national compilers interested in using Google data to enhance the timeliness and frequency of economic indicators.

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 book examines the impact of the COVID-19 pandemic on changing labour markets and accelerating digitalisation of the workplace in Central and Eastern Europe. It provides an innovative and enriching take on the work experience from the pandemic times and discusses the challenges of ongoing changes in labour markets and workplaces in a way that is not covered by the extant literature. The impact of the COVID-19 pandemic and digitalisation on labour market outcomes is analysed throughout 12 chapters, by 34 labour market experts from various CEE countries. Most chapters are based on empirical methods yet are presented in an easy-to-follow way to make the book also accessible for a non-scientific audience. The volume addresses the three key goals: to better understand the impact of the COVID-19 pandemic on the adoption of workplace digitalisation in the selected labour markets in CEE countries and the potential trade-offs facing those who do and do not have access to this benefit to complement the labour market research by incorporating the outputs of changing demand for skills to contribute new insight into policies and regulations that govern the future of work The book argues that the recent COVID-19 pandemic was a sombre reminder of the relevance and necessity of digital technology for a variety of sectors and market activities. It concludes that to downside the risks of vanishing jobs, as well as to minimise the threats and maximise the opportunities of digitalisation in CEE countries, labour market partners need to consider an effective governance tool in terms of inclusive access to the digital environment, re-skilling, and balanced regulations of the more problematic facets of digital work. The book will be of interest to postgraduate researchers and academics in the fields of labour economics, regional economics, and macroeconomics. Additionally, due to the broader policy implications of the topic, the book will appeal to policymakers and experts interested in labour economics. The Introduction, Chapters 4 and 12 of this book are freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.

The COVID-19 pandemic will cast a long shadow over the world’s economies and the economic outlook is very uncertain. This issue of the OECD Economic Outlook analyses the impacts of COVID-19 on the economy and puts forward projections for output, employment, prices, fiscal and current account balances.

Quarterly GDP statistics facilitate timely economic assessment, but the availability of such data are limited for more than 60 developing economies, including about 20 countries in sub-Saharan Africa as well as more than two-thirds of fragile and conflict-affected states. To address this limited data availablity, this paper proposes a panel approach that utilizes a statistical relationship estimated from countries where data are available, to estimate quarterly GDP statistics for countries that do not publish such statistics by leveraging the indicators readily available for many countries. This framework demonstrates potential, especially when applied for similar country groups, and could provide valuable real-time insights into economic conditions supported by empirical evidence.