Identifying Structural Vector Autoregression via Large Economic Shocks

Abstract

We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as n^2+n(n-1)/2 suitably selected moment conditions, when at least n-1 of the structural errors are all leptokurtic (or platykurtic). The potentially problematic assumption of mutually independent structural errors in part of the previous literature on statistical identification of SVAR models is also relaxed to the requirement that the errors only exhibit no excess co-kurtosis. Moreover, we assume the error term to be only serially uncorrelated, not independent in time, which allows for univariate conditional heteroskedasticity in its components. A small simulation experiment highlights the good properties of the estimator and the proposed moment selection procedure. The use of the methods is illustrated by means of an empirical application to the effect of a tax increase on U.S. gasoline consumption and carbon dioxide emissions.