Forecasting short-term inflation in Argentina with Random Forest Models

TL;DR

This study evaluates Random Forest models for short-term inflation forecasting in Argentina, showing they perform comparably to market analysts and econometric models while capturing nonlinear macroeconomic effects.

Contribution

It introduces the application of Random Forest models to inflation forecasting in Argentina, highlighting their ability to explore nonlinear relationships and variable importance.

Findings

Random Forest models achieve forecast accuracy similar to market analysts and econometric models.

The importance of exchange rate gap increases with larger deviations between parallel and official rates.

Inflation inertia and interest rates become more predictive as inflation and interest rates rise.

Abstract

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. It is found that these models achieve forecast accuracy that is statistically comparable to the consensus of market analysts' expectations 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.