The paper uses dynamic quantile regressions to estimate and forecast the conditional distribution of euro-area inflation. As in a Phillips curve relationship we assume that inflation quantiles depend on past inflation, the output gap, and other determinants, namely oil prices and the exchange rate.
We find significant time variation in the shape of the distribution. Overall, the quantile regression approach describes the distribution of inflation better than a benchmark univariate trend-cycle model with stochastic volatility, which is known to perform very well in forecasting inflation.
In an out-of-sample prediction exercise, the quantile regression approach provides forecasts of the conditional distribution of inflation that are superior, overall, to those produced by the benchmark model. Averaging the distribution forecasts of the different models improves robustness and in some cases results in the greatest accuracy of distributional forecasts.