Central banks want to learn from history. They can do so by drawing on decades of work by economic historians, as well as their own archives which manifest layers of institutional memory. But the path from page to policy can be difficult to find. Central banks need therefore to invest in the capacity of their own staff to think historically. This will help them use evidence from the past to make better decisions in the future. In practice, this means producing historical research as well as consuming it. Institutions like central banks need to be fluent participants in the conversations which bridge the distance between past and present.
In the wake of Covid-19 lockdown, macroeconomic policymakers have to deal not only with the immediate contraction in the economy, but also with the medium and longer term macro-consequences. Over the past four months, the macroeconomic literature on these topics has expanded rapidly. This post reviews the literature that considers the channels via which the shock affects the economy, and the macroeconomic policy options for dealing with the aftermath, taking as given the shock caused by the virus and the lockdown.
India Loader, from South Wilts Grammar School, is the winner of the third Bank of England/Financial Times schools blog competition. The competition invited students across the UK to write a post on the theme: the economy and climate change.
To help save the planet and gain a competitive edge, cafes should obey a basic rule of behavioural economics by switching from offering discounts for customers who bring their own cups in favour of charging more for disposable ones.
Marco Minasi-Smith, from Fortismere School, London, is the runner-up of the third Bank of England/Financial Times schools blog competition. The competition invited students across the UK to write a post on the theme: the economy and climate change.
While Australia mourns the human and ecological cost of its ‘black summer’ of fires, the tragedy poses a question for economic policy-makers everywhere: how do we prevent climate crises becoming economic ones?
The great American baseball sage, Yogi Berra, is thought to have once remarked: ‘It’s tough to make predictions, especially about the future’. That is certainly true, but thankfully the accelerating development and deployment of machine learning methodologies in recent years is making prediction easier and easier. That is good news for many sectors and activities, including microprudential regulation. In this post, we show how machine learning can be applied to help regulators. In particular, we outline our recent research that develops an early warning system of bank distress, demonstrating the improved performance of machine learning techniques relative to traditional approaches.
Whether in case of a breakup (Backstreet Boys), wondering why a relationship isn’t working (Mary J. Blige) or bad weather (Travis) – humans really care about explanations. The same holds in the world of finance, where firms increasingly deploy artificial intelligence (AI) software. But AI is often so complex that it becomes hard to explain why exactly it made a decision in a certain way. This issue isn’t purely hypothetical. Our recent survey found that AI already impacts customers – whether it’s calculating the price of an insurance policy or assessing a borrower’s credit-worthiness. In our new paper, we argue that so-called ‘explainability methods’ can help address this problem. But we also caution that, perhaps as with humans, gaining a deeper understanding of such models remains very hard.
Central banks the world over calculate and plot forecast fancharts as a way of illustrating uncertainty. Explaining the details of how this is done in a single blog post is a big ask, but leveraging free software tools means showing how to go about it isn’t. Each necessary step (getting data, building a model, forecasting with it, creating a fanchart) is shown as R code. In this post, a simple data-coherent model (a vector auto-regression or VAR) is used to forecast US GDP growth and inflation and the resulting fanchart plotted, all in a few self-contained chunks of code.
This guest post is the third of an occasional series of guest posts by external researchers who have used the Bank of England’s archives for their work on subjects outside traditional central banking topics.
George Frideric Handel was a master musician — an internationally renowned composer, virtuoso performer, and music director of London’s Royal Academy of Music, one of Europe’s most prestigious opera houses. For musicologists, studying his life and works typically means engaging with his compositional manuscripts at The British Library, as well as the documents, letters, and newspapers that describe his interaction with royalty, relationships to others, and contemporary reaction to his music. But when I began to explore Handel’s personal accounts at the Bank of England twenty years ago, I was often asked why. For me the answer was always ‘follow the money’. Handel’s financial records provide a unique window on his career, musical environments, income, and even his health.
Policymaking is invariably uncertain. I created a new index of ‘policymaker’s uncertainty’ based on a textual search of the minutes of the MPC meetings since 1997. The index is constructed by simply calculating the number of references to the word ‘uncertainty’ (and its derivatives, including ‘not certain’ and ‘far from certain’) as a share of the total word count. To avoid double-counting, it also excludes the Monetary Policy Summary that was introduced in 2015. One caveat of this approach is that it doesn’t distinguish instances of low or falling uncertainty from those where uncertainty was high. That aside, this measure can offer a new insight into uncertainty compared to indicators based on media references or business surveys.