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.
Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.
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