Aakash Mankodi and Tim Pike
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.
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.
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?
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.
Alex Haberis, Riccardo M Masolo & Kate Reinold
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.