The disaggregated time-series for the Consumer Price Index (CPI) often exhibits exact zero price changes, stemming from structural features of the data collection process. However, the currently prominent stochastic volatility model of trend-inflation is designed for aggregate measures of price inflation, where zeros rarely occur. We formulate a zero-inflated stochastic volatility model applicable to such non-stationary, real-valued, multivariate time-series data with exact zeros, which jointly specifies the dynamic zero-generating process. For posterior inference, an efficient custom Pólya--Gamma augmented Gibbs sampler is derived. Applying the model to disaggregated CPI data in four advanced economies -- US, UK, Germany, and Japan -- we find that the zero-inflated model yields more informative estimates of time-varying trend and volatility, as it accounts for the presence of zeros and avoids underestimation. In an out-of-sample forecasting exercise, we find that the zero-inflated model delivers improved point forecasts and better calibrated interval forecasts, particularly when zero-inflation is prevalent.

翻译：消费者价格指数（CPI）的分解时间序列常因数据收集过程的结构性特征而呈现精确的零价格变动。然而，当前主流的趋势通胀随机波动模型是为价格通胀的聚合度量设计的，其中极少出现零值。本文构建了一种适用于此类含精确零值的非平稳、实值、多元时间序列数据的零膨胀随机波动模型，该模型联合设定了动态零值生成过程。为进行后验推断，我们推导出一种高效的定制化Pólya-Gamma增强吉布斯采样器。将模型应用于美国、英国、德国和日本四个发达经济体的分解CPI数据时，我们发现零膨胀模型能产生更具信息量的时变趋势与波动率估计，因其考虑了零值的存在并避免了低估问题。在样本外预测实验中，零膨胀模型实现了更优的点预测与校准更佳的区间预测，尤其在零膨胀现象普遍存在时表现更为突出。