A1922
Title: Improving the robustness of Markov-switching dynamic factor models with time-varying volatility
Authors: Romain Aumond - CREST (France)
Julien Royer - CREST (France) [presenting]
Abstract: Tracking macroeconomic data at a high frequency is difficult as most time series are only available at a low frequency. Recently, the development of macroeconomic nowcasters to infer the current position of the economic cycle has attracted the attention of both academics and practitioners, with most of the central banks having developed statistical tools to track their economic situation. The models usually rely on a Markov-switching dynamic factor model with mixed-frequency data whose states allow the identification of recession and expansion periods. However, such models are famously not robust to the occurrence of extreme shocks such as Covid-19. The focus is on how the addition of time-varying volatilities in the dynamics of the model alleviates the effect of extreme observations and renders the inference of recessions more robust. Both stochastic and conditional volatility models are considered and the Bayesian estimation of the competing models is discussed. In a real data exercise, it is shown how, both in a sample and in an out-of-sample exercise, the inclusion of a GARCH component is beneficial in the identification of phases of the US economy. Additionally, the robustness of the proposed framework is investigated for various misspecifications through simulations.
