Doombot: a machine learning algorithm for predicting downturns in OECD countries
This paper describes an algorithm, “DoomBot”, which selects parsimonious models to
predict downturns over different quarterly horizons covering the ensuing two years
for 20 OECD countries. The models are country- and horizon-specific and are automatically
updated as the estimation sample period is extended, so facilitating out-of-sample
evaluation of the algorithm. A limited combination of explanatory variables is chosen
from a much larger pool of potential variables that include those that have been most
useful in predicting downturns in previous OECD work. The most frequently selected
variables are financial variables, especially those relating to credit and house prices,
but also include equity prices and various measures of interest rates (such as the
slope of the yield curve). Business cycle variables -- survey measure of capacity
utilisation, industrial production, GDP and unemployment -- are also selected, but
more frequently at very short horizons. The variables selected do not just relate
to the domestic economy of the country being considered, but also international aggregates,
consistent with findings from previous OECD work. The in-sample fit of the models
is very good on standard performance metrics, although the out-of-sample performance
is less impressive. The models do, however, provide a clear out-of-sample early warning
of the Global Financial Crisis (GFC), especially when considered collectively, although
they do generate ‘false alarms’ just ahead of the crisis. The models are less good
at predicting the euro area crisis out-of-sample, but it is clear from the evolution
of the choice of variables that the algorithm learns from this episode, for example
through the more frequent selection of a variable measuring euro area sovereign bond
spreads. The latest out-of-sample predictions made in mid-2023, suggest the probability
of a downturn is at its greatest and most widespread since the GFC, with the largest
contributions to such risks coming from house prices, interest rate developments (as
measured by the slope of the yield curve and the rapidity of the change in short rates)
and oil prices. On the other hand, warning signals from business cycle variables and
equity prices, which are often good downturn predictors at short horizons, are conspicuously
absent.
Published on December 12, 2023
In series:OECD Economics Department Working Papersview more titles