Forecasting Inflation with a Simple and Accurate Benchmark: A Cross-Country Analysis

We explore the ability of several univariate models to predict inflation in a number of countries and at several forecasting horizons. We place special attention on forecasts coming from a family of ten seasonal models that we call the Driftless Extended Seasonal ARIMA (DESARIMA) family. Using out-of-sample Root Mean Squared Prediction Errors (RMSPE) we compare the forecasting accuracy of the DESARIMA models with that of traditional univariate time-series benchmarks available in the literature. Our results show that DESARIMA-based forecasts display lower RMSPE at short horizons for every single country, except one. We obtain mixed results at longer horizons. Roughly speaking, in half of the countries, DESARIMA-based forecasts outperform the benchmarks at long horizons. Remarkably, the forecasting accuracy of our DESARIMA models is surprisingly high in stable inflation countries, for which the RMSPE is barely higher than 100 basis points when the prediction is made 24- and even 36-months ahead.