Argentina | Forecasting short-term inflation with Random Forest Models

This paper examines the performance of Random Forest models in forecasting short-term monthly inflation in Argentina, based on a database of monthly indicators since 1962.

Key points

• Key points:
• It is found that these models achieve forecast accuracy that is statistically comparable to the consensus of market analysts surveyed by the Central Bank of Argentina (BCRA) and to traditional econometric models.
• One advantage of Random Forest models is that, as they are non-parametric, they allow for the exploration of nonlinear effects in the predictive power of certain macroeconomic variables on inflation.
• Among other findings, the relative importance of the exchange rate gap in forecasting inflation increases when the gap between the parallel and official exchange rates exceeds 60%.
• The predictive power of the exchange rate on inflation rises when the BCRA’s net international reserves are negative or close to zero (specifically, below USD 2 billion).
• The relative importance of inflation inertia and the nominal interest rate in forecasting the following month's inflation increases when the nominal levels of inflation and/or interest rates rise.

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