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. We identify a high frequency shock in a Monte Carlo experiment and in an illustrative example with 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.
Keywords: Bayesian mixed-frequency VAR, MIDAS, Monte Carlo, uncertainty shocks, macro-financial linkages.
JEL codes: EC32, E44, E52.
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