Jin-Kyu Jung, Manasa Patnam, and Anna Ter-Martirosyan
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook.
Most traditional forecasting models rely on fitting data to a pre-specified relationship between input
and output variables, thereby assuming a specific functional and stochastic process underlying that
process. We pursue a new approach to forecasting by employing a number of machine learning
algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true
relationship between input and output variables. We apply the Elastic Net, SuperLearner, and
Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and
emerging economies and find that these algorithms can outperform traditional statistical models,
thereby offering a relevant addition to the field of economic forecasting.
Mr. Sergi Lanau, Adrian Robles, and Mr. Frederik G Toscani
We study inflation dynamics in Colombia using a bottom-up Phillips curve approach. This
allows us to capture the different drivers of individual inflation components. We find that the
Phillips curve is relatively flat in Colombia but steeper than recent estimates for the U.S.
Supply side shocks play an important role for tradable and food prices, while indexation
dynamics are important for non-tradable goods. We show that besides allowing for a more
detailed understanding of inflation drivers, the bottom-up approach also improves on an
aggregate Phillips curve in terms of forecasting ability. In the baseline forecast scenario, both
headline and core inflation converge towards the Central Bank’s inflation target of 3 percent
by end-2018 but these favorable inflation dynamics are vulnerable to large supply shocks.