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1. Introduction
What are the key drivers of inflation? And which role do expectations play
in the inflation process? These have been long standing questions in
macroeconomics, particularly given their high relevance to economic policy
making. Indeed, the paper that is often credited with having started the rational
expectations revolution (Muth (1961)) was concerned exactly with the above
questions.
The current study attempts to shed some fresh light on these core
macroeconomic questions. It does so through the lens of a flexible non-parametric
data driven method. Specifically, it applies the well-established random forest
approach (Breiman et al (1984), Breiman (2001)) to disentangle the drivers of
inflation since 2000 across 20 advanced economies. Beyond comparing
explanatory performance with traditional econometric benchmarks, as far as
possible, it tries to interpret the economic reasons that are behind the (relative)
success of the technique in explaining recent consumer price inflation.
Overall, the analysis attests the relative strong performance of the random
forest model in predicting contemporaneous and future headline and core CPI
inflation, even when only a small standard set of macroeconomic indicators is
used. In fact, the out of sample root mean square error (RMSE) of the machine
learning (ML) model beats even the in-sample performance of standard OLS using
the same set of explanatory variables/features − which are firmly grounded on
economic theory. This suggests that non-linearities play an important role in
explaining inflation.
Overall, expectations emerge as the most important predictor of CPI
inflation, followed by past inflation. That said, the importance of expectations has
declined during the last 10 years. During this period, the partial effects that are
teased out from the random forest model point to a flattening of the effects of
expectations when these are above 2%. Throughout, exchange rate variations are
found to add relatively little value in predicting inflation outcomes.
Relation to the literature. The paper builds on a growing literature that applies
machine learning (ML) to economics. Kleinberg et al (2015) discuss the
advantages and caveats of applying ML techniques to economic prediction
problems. They argue that ML provides a disciplined non-parametric way to
predict economic outcomes. Mullainathan and Spiess (2017) offer an example of
how regression trees can be used to better predict house prices. They conclude
their review by stating that “machine learning provides a powerful tool to hear,
more clearly than ever, what the data have to say”. As such, it can be a useful
complement to more traditional model based methods.
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Earlier, Varian (2014) provided an example of regression trees for predicting mortgage
approvals.
