Can long-run restrictions identify technology shocks?

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 using calibrated business cycle models. Overall, Gali's methodology appears to be fruitful: the impulse responses derived from the artificial data generally have the same sign and qualitative pattern as the true responses, and the approach can be informative in discriminating between alternative models. However, our results reveal some important quantitative shortcomings, including considerable estimation uncertainty about the impact of technology shocks on macroeconomic variables. More generally, the conditions under which the methodology performs well appear considerably more restrictive than implied by the key identifying assumption. This underscores the importance of using economic models to guide in the implementation of the approach, in interpreting the results, and in assessing its limitations.