Forecasting short-term inflation in Argentina with Random Forest Models
利用随机森林模型预测阿根廷短期通胀
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…