Global smartphone sales may have peaked. After reaching nearly 1.5 billion units in 2016, global smartphone sales have since declined, contributing negatively to world trade in 2019 and suggesting that the global market may now be saturated. This paper develops a simple model to forecast smartphone sales, which shows that sales are likely to decline further. As tech companies shift to embedded services (cloud computing, content subscriptions, and financial services), the impact on global trade may also be shifting in favor of services exports mostly from advanced economies.
This paper introduces a simple, frequently and easily updated, close to the data epidemiological model that has been used for near-term forecast and policy analysis. We provide several practical examples of how the model has been used. We explain the epidemic development in the UK, the USA and Brazil through the model lens. Moreover, we show how our model would have predicted that a super infectious variant, such as the delta, would spread and argue that current vaccination levels in many countries are not enough to curb other waves of infections in the future. Finally, we briefly discuss the importance of how to model re-infections in epidemiological models.
This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade. While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have strong explanatory power to forecast world trade and national trade in selected economies. The informational content from documentary collections helps improve the forecast of world trade, while a horse race with machine learning algorithms shows significant non-linearities between trade and its determinants during the Covid-19 pandemic.
An essential element of the work of the Fund is to monitor and forecast international trade. This paper uses SWIFT messages on letters of credit, together with crude oil prices and new export orders of manufacturing Purchasing Managers’ Index (PMI), to improve the short-term forecast of international trade. A horse race between linear regressions and machine-learning algorithms for the world and 40 large economies shows that forecasts based on linear regressions often outperform those based on machine-learning algorithms, confirming the linear relationship between trade and its financing through letters of credit.
Global smartphone sales may have peaked. After reaching nearly 1.5 billion units in 2016, global smartphone sales have since declined, contributing negatively to world trade in 2019 and suggesting that the global market may now be saturated. This paper develops a simple model to forecast smartphone sales, which shows that sales are likely to decline further. As tech companies shift to embedded services (cloud computing, content subscriptions, and financial services), the impact on global trade may also be shifting in favor of services exports mostly from advanced economies.
This paper introduces a simple, frequently and easily updated, close to the data epidemiological model that has been used for near-term forecast and policy analysis. We provide several practical examples of how the model has been used. We explain the epidemic development in the UK, the USA and Brazil through the model lens. Moreover, we show how our model would have predicted that a super infectious variant, such as the delta, would spread and argue that current vaccination levels in many countries are not enough to curb other waves of infections in the future. Finally, we briefly discuss the importance of how to model re-infections in epidemiological models.
This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade. While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have strong explanatory power to forecast world trade and national trade in selected economies. The informational content from documentary collections helps improve the forecast of world trade, while a horse race with machine learning algorithms shows significant non-linearities between trade and its determinants during the Covid-19 pandemic.
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