We show that under standard assumptions the elements of the impact matrix of the structural vector autoregression (SVAR) are always at least set identified and bounded by the standard deviations of the corresponding reduced form errors. This result facilitates valid Bayesian inference without...

We develop a similarity-based structural vector autoregressive (SVAR) model using the similar clusters of data relevant for the prevailing initial macroeconomic conditions of interest. Our computationally attractive simple approach enables us to uncover time-varying effects of structural...

We introduce a simple nonparametric approach to compute impulse response functions. We first search for clusters of recurrent patterns of observations resembling two sets of given initial conditions, one of which contains the impact effect of the structural shock of interest. Then, to trace out...

We introduce a structural vector autoregressive (SVAR) model with each of the mutually independent errors following a skewed generalized t-distribution that is more flexible than a Student's t-distribution typically considered. Hence, the effect of potential distributional misspecification is...

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...

In structural vector autoregressive (SVAR) models identifying restrictions for shocks and impulse responses are usually derived from economic theory or institutional constraints. Sometimes the restrictions are insufficient for identifying all shocks and impulse responses. In this paper it is...