56 Pages Posted: 17 Feb 2004
Date Written: January 2004
Galí'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 using calibrated business cycle models. We find it encouraging that the impulse responses derived from applying the Galí methodology to the artificial data generally have the same sign and qualitative pattern as the true responses. However, we find considerable estimation uncertainty about the quantitative impact of a technology shock on macroeconomic variables, and little precision in estimating the contribution of technology shocks to business cycle fluctuations. More generally, our analysis emphasizes that the conditions under which the methodology performs well appear considerably more restrictive than implied by the key identifying assumption, and depend on model structure, the nature of the underlying shocks, and variable selection in the VAR. This cautions against interpreting responses derived from this approach as model-independent stylized facts.
Keywords: Technology shocks, vector autoregressions, real business cycle models
JEL Classification: E32, C15, C52
Suggested Citation: Suggested Citation
Erceg, Christopher J. and Guerrieri, Luca and Gust, Christopher J., Can Long-Run Restrictions Identify Technology Shocks? (January 2004). Board of the Governors of the Federal Reserve International Finance Discussion Paper. Available at SSRN: https://ssrn.com/abstract=502624 or http://dx.doi.org/10.2139/ssrn.502624