Inference Based on SVARs Identied with Sign and Zero Restrictions: Theory and Applications

Are optimism shocks an important source of business cycle fluctuations? Are decit-nanced tax cuts better than decit-nanced spending to increase output? These questions have been previously studied using SVARs identied with sign and zero restrictions and the answers have been positive and denite in both cases. While the identication of SVARs with sign and zero restrictions is theoretically attractive because it allows the researcher to remain agnostic with respect to the responses of the key variables of interest, we show that current implementation of these techniques does not respect the agnosticism of the theory. These algorithms impose additional sign restrictions on variables that are seemingly unrestricted that bias the results and produce misleading condence intervals. We provide an alternative and ecient algorithm that does not introduce any additional sign restriction, hence preserving the agnosticism of the theory. Without the additional restrictions, it is hard to support the claim that either optimism shocks are an important source of business cycle fluctuations or decit-nanced tax cuts work best at improving output. Our algorithm is not only correct but also faster than current ones.