A0405
Title: Identifying high-frequency shocks in nonlinear models
Authors: Alessia Paccagnini - University College Dublin (Ireland) [presenting]
Fabio Parla - University of Palermo (Italy)
Abstract: Focusing on mixed frequency regressions, a novel approach implemented in a nonlinear setting is proposed. Applying Bayesian techniques, we identify high-frequency shocks studying the behavior of the temporal aggregation bias across the business cycle phases. Montecarlo experiments provide an assessment of the results using different Data Generation Processes. The empirical illustration provides new evidence to identify the effects of the uncertainty shock on the US economy's macro variables disentangling between normal times, recessionary times, and disaster events.
