A1080
Title: Inflation target at risk: A time-varying parameter distributional regression
Authors: Dan Zhu - Monash University (Australia)
Tatsushi Oka - Keio University (Japan)
Yunyun Wang - Hunan Univeristy (China) [presenting]
Abstract: Macro variables frequently display time-varying distributions, driven by the dynamic and evolving characteristics of economic, social, and environmental factors that consistently reshape the fundamental patterns and relationships governing these variables. To better understand the distributional dynamics beyond the central tendency, a novel semiparametric approach is introduced for constructing time-varying conditional distributions, relying on the recent advances in distributional regression. An efficient precision-based Markov chain Monte Carlo algorithm is presented that simultaneously estimates all model parameters while explicitly enforcing the monotonicity condition on the conditional distribution function. The model is applied to construct the forecasting distribution of inflation for the U.S., conditional on a set of macroeconomic and financial indicators. The analysis evaluates the risks of future inflation deviating excessively above or below the desired target range and identifies key risk drivers through an assessment of potential covariates.
