ABSTRACT This study proposes an alternative procedure to identify technology shocks using vector autoregressions (VARs). The proposed procedure delivers improved small-sample properties relative to the standard long-run identification method provided that the dynamics of the observed variables can only be captured precisely by an infinite-order VAR. Monte Carlo experiments on artificial data produced by a standard version of the real business cycle model demonstrate that the proposed procedure is associated with smaller average bias and mean square error. These results obtain under a range of specifications regarding the share of technology shocks in overall output variability.
Cite this paper
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