Machine learning the news for better macroeconomic forecasting

Arthur Turrell, Eleni Kalamara, Chris Redl, George Kapetanios and Sujit Kapadia

Every day, journalists collate information about the world and, with nimble keystrokes, re-express it succinctly as newspaper copy. Events about the macroeconomy are no exception. So could there be additional valuable information about the economy contained in the news? In a recent research paper, we ask whether newspaper stories could help to predict future macroeconomic developments. We find that news can be used to enhance statistical economic forecasts of growth, inflation and unemployment — but only by using supervised machine learning techniques. We also find that the biggest forecast improvements occur when it matters most — during stressed periods.

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‘The world turned upside down’: How the global economy was hit by the crisis

David Young

For the global economy, it was the best of times, and then it was the worst of times. Buoyed by very strong growth in emerging markets, the global economy boomed in the mid-2000s. On average, annualised world GDP growth exceeded 5% for the four years leading up to 2007 – a pace of growth that hadn’t been sustained since the early 1970s. But it wasn’t to last. In this post, I illustrate how the failure of Lehman Brothers in September 2008 coincided with the deepest, most synchronised global downturn since World War II. And I describe how after having seen the fallout of the Lehman collapse, macroeconomic forecasters were nevertheless surprised by the magnitude of the ensuing global recession.

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Bitesize: Power and progress

Arthur Turrell.

Energy is the fundamental currency of the physical world, while GDP is the imperfect catch-all measure of economic progress. The plot shows electricity generation per capita against GDP per capita for 2015. The bubble areas represent population size, while the colours are the fraction of power which is produced from renewable sources – with light green a high percentage and dark green a low percentage.

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Rewriting history: understanding revisions to UK GDP

Seeing into the future is always difficult. But in the world of macroeconomics, just trying to look at the past can be a challenge. Official estimates of economic growth in the UK are regularly revised, so forecasts for growth over the next year have to be made on the basis of an ever-changing report card for the previous year. This post tackles some of the most common questions about UK GDP revisions, a topic close to the heart of many users of the UK’s National Accounts. Are the initial estimates of growth biased? Can you predict revisions? Does UK data get revised more than other countries? And which parts of early estimates of GDP should be approached with caution?

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Forecasting GDP in the presence of breaks: when is the past a good guide to the future?

George Kapetanios, Simon Price and Sophie Stone.

Structural breaks are a major source of forecast errors, and few come larger than the recent financial crisis and subsequent recession. After a break, formerly good models stop working. One way to cope is to discount the past in a data driven way. We try that, and find that shortly after the crash it was best to ignore almost all data older than three years – but now it is again time to take a longer view.
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