News shocks , identification , structural vector autoregressive model
Abstract:
In an influential recent paper, Beaudry and Portier (2006) propose a sequential approach for identifying
technological news shocks. Thereby, the correlation coefficient between news shocks of a
short-run identification scheme and technology shocks of a long-run identification scheme in the
VAR framework measures the extent to which news incorporated into forward-looking variables
could reflect future technological developments. While structural VARs can potentially provide a
useful guide for modelers as well as policy-makers, the ability of such models to recuperate structural
shocks in general and news shocks in particular from the data is a contentious issue in the
literature. In the current paper, I find by means of Monte Carlo simulations that the sequential
approach can be quite successful in recuperating technological news shocks from artificial data.
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