A1530
Title: Advances in modeling time-varying trends using large VARs
Authors: Marta Banbura - European Central Bank (Germany) [presenting]
Joshua Chan - Purdue University (United States)
Bowen Fu - CEFMS, Hunan University (China)
Abstract: Measuring macroeconomic trends in a rapidly changing environment is challenging, as it is difficult to disentangle abrupt trends from outliers. The aim is to tackle this challenge by developing a novel steady-state Bayesian VAR with a number of important features. First, the model incorporates a hierarchical shrinkage prior to the time-varying trends that favour smooth trend transitions while it is also capable of detecting abrupt changes. Second, it features an outlier component that can address extreme observations such as COVID-19 outliers. Third, it builds upon an order-invariant stochastic volatility specification, as opposed to the commonly used Cholesky-based stochastic volatility models under which trend estimates may depend on how the endogenous variables enter the system. The methodology is illustrated using US and EA disaggregated inflation data.
