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 of several relatively standard 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 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.
In this paper, we describe a new multi-country open economy SDGE model named "SIGMA" that we have developed as a quantitative tool for policy analysis. We compare SIGMA's implications to those of an estimated large-scale econometric policy model (the FRB/Global model) for an array of shocks that are often examined in open-economy policy simulations. We show that SIGMA's implications for the near-term (2-3 year) responses of key variables are generally similar to those of FRB/Global. Two features of our modeling framework, including rational expectations with learning, and the inclusion of some non-Ricardian agents, play an important role in giving SIGMA more flexibility to generate responses akin to the econometric policy model; nevertheless, some quantitative disparities between the two models remain due to certain restrictive aspects of SIGMA's optimization-based framework. We conclude by using long-term simulations to illustrate some areas of comparative advantage of our SDGE modeling framework. These include linking model responses to underlying structural features of the economy, and fully articulating the endogenous channels through which "imbalances" arising from various shocks are alleviated.
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