Category: New Methodologies

When linear models are misleading

Jeremy Chiu and Sinem Hacioglu Hoke.

When shocks cause trouble

Small shocks can lead to big crises. At the heart of this issue is that economic dynamics might play out very differently against different backdrops: the same shock would have a very different effect if it hit the economy at the heights of the Great Recession than if it hit during more benign times. It might knock the economy into a more severe and persistent recession or financial stress if it hits already turbulent periods. It seems reasonable, therefore, that we would want to take into account the economic backdrop when we estimate our models.

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A dynamic model of financial balances for the United Kingdom

Stephen Burgess, Oliver Burrows, Antoine Godin, Stephen Kinsella and Stephen Millard.

How can large open economies deal with persistent imbalances now and into the future? This question became particularly pertinent in the Great Moderation where, despite stability in output and inflation, sectoral financial balances, both within and across countries, widened. In a recent Staff Working Paper, we developed a model of the UK economy to assess how economic and financial imbalances are likely to evolve over longer periods.  Here, we show how we can use this model to examine the evolution of financial balances under different scenarios.  We think models like this can form a useful addition to the suite of models called upon by policy-makers to help in their decision making.

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DIY Macroeconomic Modelling on a Raspberry Pi

Andrew Blake.

Enthusiasts use the tiny Raspberry Pi computers for many things.  Fun ones include garage door opening, retro gaming, a voice-activated tea maker, live images from near-space and even a GPS kitten tracker.  These computers are primarily educational but do anything a normal computer does, so users also send email, play Minecraft, program and (it turns out) do macroeconomic modelling.

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How credit risk can incentivise banks to keep making payments at the height of a crisis

Marius Jurgilas, Ben Norman and Tomohiro Ota.

The final, practical determinant of whether a bank is a going concern is: does it have the liquidity to make its payments as they become due?  Thus, the ultimate crucible in which financial crises play out is the payment system.  At the height of recent crises, some banks delayed making payments for fear of paying to a bank that would fail (Norman (2015)).  This post sets out a design feature in a payment system that creates incentives, especially during financial crises, for banks to keep making payments.  This feature could address situations where banks in the system would otherwise be tempted to postpone their payments to a bank that is (rumoured to be) in trouble.

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Balancing bias and variance in the design of behavioral studies: The importance of careful measurement in randomized experiments

Andrew Gelman.
bu-guest-post2

The Centre for Central Banking Studies recently hosted their annual Chief Economists Workshop, whose theme was “What can policymakers learn from other disciplines”.  In this guest post, one of the keynote speakers at the event, Andrew Gelman professor of statistics and political science at Columbia University, points out some of the pitfalls of randomly assigned experiments with control groups.

When studying the effects of interventions on individual behavior, the experimental research template is typically:  Gather a bunch of people who are willing to participate in an experiment, randomly divide them into two groups, assign one treatment to group A and the other to group B, then measure the outcomes.  If you want to increase precision, do a pre-test measurement on everyone and use that as a control variable in your regression.  But in this post I argue for an alternative approach- study individual subjects using repeated measures of performance, with each one serving as their own control.

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It’s time to bring more realistic models of human behaviour into economic policy and regulation

David Halpern.

bu-guest-post2The Centre for Central Banking Studies recently hosted their annual Chief Economists Workshop, whose theme was “What can policymakers learn from other disciplines”.  In this guest post, one of the keynote speakers at the event, David Halpern, CEO of the Behavioural Insights Team, argues that insights from behaviour science can improve the design and effectiveness of economic policy interventions.

Behaviour science has had major impacts on policy in recent years. Introducing a more realistic model of human behaviour – to replace the ‘rational’ utility-maximizer – has enabled policymakers to boost savings; increase tax payments; encourage healthier choices; reduce energy consumption; boost educational attendance; reduce crime; and increase charitable giving. But there remain important areas where its potential has yet to be realised, including macroeconomic policy and large areas of regulatory practice. Businesses, consumers, and even regulators are subject to similar systematic biases to other humans. These include overconfidence; being overly influenced by what others are doing; and being influenced by irrelevant information. The good news is that behavioural science offers the prospect of helping regulators address some of their most pressing issues. This includes: anticipating and addressing ‘animal spirits’ that drive bubbles or sentiment-driven slowdowns; reducing corrupt market practices; and encouraging financial products that are comprehensible to humans.

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Matchmaker, matchmaker make me a mortgage: What policymakers can learn from dating websites

Angelina Carvalho, Chiranjit Chakraborty and Georgia Latsi.

Policy makers have access to more and more detailed datasets. These can be joined together to give an unprecedentedly rich description of the economy. But the data are often noisy and individual entries are not uniquely identifiable. This leads to a trade-off: very strict matching criteria may result in a limited and biased sample; making them too loose risks inaccurate data. The problem gets worse when joining large datasets as the potential number of matches increases exponentially. Even with today’s astonishing computer power, we need efficient techniques. In this post we describe a bipartite matching algorithm on such big data to deal with these issues. Similar algorithms are often used in online dating, closely modelled as the stable marriage problem.

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Is finance a powerful driver of growth?

Saleem Bahaj, Iren Levina and Jumana Saleheen.

Since the financial crisis the UK has experienced a period of weak productivity growth, weak investment coupled with a decline in credit to non-financial sectors of the economy.  But there is debate about the direction of causality: did low growth and other structural factors mean firms and households wanted to borrow less – as argued by Martin Wolf?  Or did the financial sector offer too few funds to the real economy in the wake of the crisis as banks tried to repair their balance sheets. Alternatively, the financial system may not be functioning properly in general, if much of the financial sector’s activity contributes little to the betterment of lives and efficiency of business – a point made by John Kay.

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Life below zero – the impact of negative rates on derivatives activity

James Purchase and Nick Constantine.

In 1995, Fischer Black, an economist whose ground-breaking work in financial theory helped revolutionise options trading, confidently stated that “the nominal short rate cannot be negative.”  Twenty years later this assumption looks questionable: one quarter of world GDP now comes from countries with negative central bank policy rates.  Practitioners have been forced to update their models accordingly, in many cases introducing greater complexity.  But this shift is not just academic.  Models allowing for a wider distribution of future rates require market participants to hedge against greater uncertainty.  We argue that this hedging contributed to the volatility in global rates in early 2015, but that derivatives can also play an important role in facilitating monetary policy transmission at negative rates.

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