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.
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.
Tetlock and Gardner’s acclaimed work on Superforecasting provides a compelling case for seeing forecasting as a skill that can be improved, and one that is related to the behavioural traits of the forecaster. These so-called Superforecasters have in recent years been pitted against experts ranging from U.S intelligence analysts to participants in the World Economic Forum, and have performed on par or better by accurately predicting the outcomes of a broad range of questions. Sounds like music to a central banker’s ears? In this post, we examine the traits of these individuals, compare them with economic forecasting and draw some related lessons. We conclude that considering the principles and applications of Superforecasting can enhance the work of central bank forecasting.
Christopher Hackworth, Nicola Shadbolt and David Seaward.
While official housing market statistics are relatively timely and high frequency, they usually come with a lag of at least one month. So indicators that lead official estimates are helpful for identifying turning points, or any ‘shocks’ to the economy.
Nicholas Fawcett, Riccardo Masolo, Lena Koerber, Matt Waldron.
Introduction: forecasting and policy-making
Forecasting is difficult, especially when it concerns the future. If we needed a reminder, the 2008-09 financial crisis demonstrated that macroeconomic forecasts can be highly inaccurate when the economy is buffeted by large shocks (see, for example, Figure 1). But that is not a good reason to avoid forecasting: monetary policy takes time to work, so forecasts are indispensable in monetary policymaking. Instead, we need to understand how different models behave in the eye of the storm: do some cope better during breaks and crises than others? And can we make better forecasts by using information that is not normally included in economic models?
Inflation is currently very low in the UK (indeed briefly dipping into negative territory in April), naturally raising speculation about whether we will experience persistent deflation in coming years. This post illustrates that the probability of deflation is raised further, and the likely duration of any deflation increased, if one thinks that there are limits on how far the Monetary Policy Committee (MPC) could loosen policy in the face of new shocks. We also explore how the current situation differs from other episodes since the crisis when the risk of deflation has been similarly elevated.