Alessia Paccagnini, University College Dublin
Project Description/Abstract
We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. We impose a Normal-inverse Wishart prior by adding a set of auxiliary dummies in estimating a Mixed-Frequency VAR. Based on this new “high-frequency” identification scheme, we illustrate our method by identifying uncertainty shock for the U.S. economy. As the main findings, we document a “temporal aggregation bias” when we adopt a common low-frequency model instead of estimating a mixed-frequency framework. The bias is amplified in case of a large mismatching between the high-frequency shock and low-frequency business cycle variables. Link paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3855847
Co-author
Fabio Parla, Central Bank of Ireland