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

We evaluate the ability of several univariate models to predict inflation in a number of countries and at several forecasting horizons. We focus 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 case. We obtain mixed results at longer horizons. In particular, when the median forecast is considered, in more than half of the countries our DESARIMA-based forecasts outperform the benchmarks at long horizons. Remarkably, the forecasting accuracy of our DESARIMA models is high in stable inflation countries, for which the RMSPE is around 100 basis points when prediction is made 24- and even 36-months ahead