A1737
Title: A real-time monitoring test for common breaks and factors in panel data
Authors: Cindy Shin Huei Wang - HSBC Business School, Peking University (China) [presenting]
Silvia Bacci - University of Perugia (Italy)
Abstract: A novel real-time monitoring test is established for detecting common breaks or factors within a panel-data framework via a panel autoregressive (PAR) approximation framework. The limiting distribution of this real-time monitoring test follows a Brownian bridge and is free of model parameters if there is no common break or factor within a panel. Its convincing finite sample performance has been confirmed through Monte Carlo simulations, even though there exist multiple breaks within a panel system. An empirical application to monitor the mean-variance convergence around the world during the period of the global crisis, including the 2007-2008 subprime crisis, the 2018-2019 Sino-US war trade, and the recent COVID-19 pandemic crisis, demonstrates the usefulness and feasibility of our real-time monitoring procedure.
