Inflation exhibits state-dependent, skewed, and fat-tailed dynamics that make risk a central concern for monetary policy. Accordingly, inflation risks are distributional and cannot be fully captured by mean-based models. We propose a flexible time-varying parameter distributional regression model that estimates the full conditional distribution of inflation, allowing macroeconomic drivers to have nonlinear and asymmetric effects across the distribution. Applied to U.S. inflation, the model captures major shifts in tail-risk probabilities. Analysis of risk drivers shows that deflationary pressures arise primarily from demand-side weakness and inflation persistence, whereas upside risks are driven mainly by supply-side shocks, particularly energy price inflation. Examining the impact of key drivers further reveals that the unemployment-inflation relationship weakens in the distributional tails. Energy price shocks, by contrast, have little effect on deflation risk but exhibit strongly time-varying and asymmetric effects on high-inflation risk.

翻译：通胀展现出状态依赖、偏态和厚尾的动态特征，使得风险成为货币政策的核心关切。因此，通胀风险是分布性的，无法被基于均值的模型完全捕捉。我们提出了一种灵活的时变参数分布回归模型，该模型估计通胀的完整条件分布，允许宏观经济驱动因素在分布上产生非线性和非对称效应。应用于美国通胀数据时，该模型捕捉到了尾部风险概率的重大转变。对风险驱动因素的分析表明，通缩压力主要源于需求侧疲软和通胀持续性，而上行风险则主要由供给侧冲击驱动，尤其是能源价格通胀。进一步考察关键驱动因素的影响发现，失业与通胀的关系在分布尾部减弱。相比之下，能源价格冲击对通缩风险影响甚微，但对高通胀风险表现出强烈的时变和非对称效应。