https://macropaperwarehouse.com/authors/maximilian-schroder/Maximilian SchröderHugo -- gohugo.ioen-ushttps://macropaperwarehouse.com/papers/mixing-it-up-inflation-at-risk/https://macropaperwarehouse.com/papers/mixing-it-up-inflation-at-risk/<p>This paper introduces a Bayesian Gaussian mixture density regression framework that estimates the complete forecast distribution of inflation — not just selected quantiles — and decomposes the entire risk outlook into contributions from individual economic predictors. The methodology accommodates multimodality, skewness, and fat tails without parametric restrictions, and allows construction of risk measures calibrated to the central bank&rsquo;s own loss function rather than generic percentile-based measures. Applied to the recent U.S. inflation surge, the framework finds that post-pandemic inflation risk was primarily driven by the recovery of the U.S. business cycle and surging commodity prices, while adjustments in monetary policy contributed negatively — partially mitigating the increase in right-tail inflation risk — and credit spreads also offset some risk. The Gaussian mixture structure enables fast MCMC estimation and produces well-calibrated density forecasts across a range of macroeconomic variables.</p>