The economic consequences of the Russia-Ukraine war have brought the importance of sharp changes in commodity prices, such as oil, to centre stage. While many have focused on understanding the impact of these developments on the central projection for the macroeconomic outlook, this post investigates the balance of risks arising from oil-supply shocks, asking: could these lead to more severe or persistent changes in output growth and inflation, in rare events? Through the lens of a simple statistical model of Inflation- and GDP-at-Risk, we quantify the macroeconomic risks to inflation and GDP growth associated with (exogenous) changes in oil supply, showing that these shocks have more pronounced effects on the upper tail of the inflation distribution than at the centre.
Nikoleta Anesti, Marco Garofalo, Simon Lloyd, Edward Manuel and Julian Reynolds
Understanding and quantifying risks to the economic outlook is essential for effective monetary policymaking. In this post, we describe an ‘Inflation-at-Risk’ model, which helps us assess the uncertainty and balance of risks around the outlook for UK inflation, and understand how this uncertainty relates to underlying economic conditions. Using this data-driven approach, we find that higher inflation expectations are particularly important for driving upside risks to inflation, while a widening in economic slack is important for downside risks. Our model highlights that rising tail-risks can become visible before a turning point, making the approach a useful addition to economists’ forecasting toolkit.
Systemic financial crises occur infrequently, giving relatively few crisis observations to feed into the models that try to warn when a crisis is on the horizon. So how certain are these models? And can policymakers trust them when making vital decisions related to financial stability? In this blog, I build a Bayesian neural network to predict financial crises. I show that such a framework can effectively quantify the uncertainty inherent in prediction.
The Citizens’ Panels (now the Citizens’ Forum) is a Bank of England discussion forum to engage with the UK public on important topics such as the labour and housing markets, or climate change. It included a forecasting competition, and Bank Underground invited the winners to contribute short pieces about how they evaluate the UK economy, discuss issues of their concern, and to propose solutions.
Part of Bank Underground’s purpose is to give a platform for views from Bank of England (‘Bank’) analysts that may differ from those of the Bank or its policy committees. Alternative views are encouraged within the Bank, but the range of opinions and ways of thinking by analysts is likely to be limited to some extent: by education, experience and less tangible factors such as the language analysts use to explain their thoughts. The Citizens’ Panels therefore offer a rich source of information. By now, they include some 3,200+ participants with a wide range of backgrounds: some are familiar with economics and central banking but many may know little about either. This blog represents the voices of some of those panel members about the UK economy, and how they addressed the forecasting challenge, which we put in front of participants as part of the Citizens’ Forum online community – which by-the-way is open to all.
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?