2024/03/16 by Han, Geonhee, Irie, Kaoru
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.2403.10945
The disaggregated time-series data for Consumer Price Index (CPI) often exhibits frequent instances of exact zero price changes, stemming from measurement errors inherent in the data collection process. However, the currently prominent stochastic volatility model of trend inflation is designed for aggregate measures of price inflation, where exact zero price changes rarely occur. We formulate a zero-inflated stochastic volatility model applicable to such non-stationary real-valued multivariate time-series data with exact zeros. The Bayesian dynamic generalized linear model jointly specifies the dynamic zero-generating process. We construct an efficient custom Gibbs sampler, leveraging the Pólya-Gamma augmentation. Applying the model to disaggregated CPI data in four advanced economies -- US, UK, Germany, and Japan -- we find that the zero-inflated model provides more sensible and informative estimates of time-varying trend and volatility. Through an out-of-sample forecasting exercise, we find that the zero-inflated model delivers improved in point forecasts and better-calibrated interval forecasts, particularly when zero-inflation is prevalent.