Can Long-Run Restrictions Identify Technology Shocks?

Christopher John Erceg and Luca Guerrieri ()

No 3, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: Gali's innovative approach of imposing long-run restrictions on a vector autoregression (VAR) to identify the effects of a technology shock has become widely utilized. In this paper, we investigate its reliability through Monte Carlo simulations of several relatively standard business cycle models. We find it encouraging that the impulse responses derived from applying the Gali methodology to the artificial data generally have the same sign and qualitative pattern as the true responses. However, we highlight the importance of small-sample bias in the estimated impulse responses and show that the magnitude and sign of this bias depend on the model structure. Accordingly, we caution against interpreting responses derived from this approach as ``model-independent'' stylized facts. Moreover, we find considerable estimation uncertainty about the quantitative impact of a technology shock on macroeconomic variables, and a corresponding level of uncertainty about the contribution of technology shocks to the business cycle

Keywords: technology shocks; vector autoregressions; real business cycles (search for similar items in EconPapers)
JEL-codes: E30 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge
Date: 2004-08-11
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Related works:
Working Paper: Can long-run restrictions identify technology shocks? (2004)
Journal Article: Can Long-Run Restrictions Identify Technology Shocks? (2005)
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