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Title: Identifying structural vector autoregression via leptokurtic economic shocks Authors:  Markku Lanne - University of Helsinki (Finland) [presenting]
Keyan Liu - University of Helsinki (Finland)
Jani Luoto - University of Helsinki (Finland)
Abstract: The generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model is revisited. 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). We also relax the potentially problematic assumption of mutually independent structural errors in part of the previous literature to the requirement that the errors be mutually uncorrelated. 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.