Forecasting Inflation From Disaggregated Data

ABSTRACT We forecast inflation aggregates for the United States, the United Kingdom, and Colombia using forecasts aggregation of disaggregates and forecasts obtained directly from the aggregate. We implement helpful models for many predictors, such as dimension reduction, shrinkage methods, machine learning models, and traditional time‐series models (ARIMA and TAR). We evaluate out‐sample forecasts for the period before COVID‐19 and the period afterward. It was found that the aggregation of forecasts performs as well as the forecast using the aggregate directly. In some cases, there is a reduction in the forecast error from the disaggregate analysis.