Abstract
A new approach is proposed for estimating bivariate SVAR systems with Gaussian distribution shocks. Each sequence of independent shocks has two unknown variance parameters and a structural matrix also leaves their non-diagonal elements unknown. So, there are four unknown parameters, while the symmetric covariance matrix of the residuals in reduced VAR gives us three components. We propose an estimation method such that a new variable with independent Gaussian distributions is added to the original 2-d system. By setting a structural matrix with three unknown parameters and taking three variance parameters into consideration, the number of unknwn parameters becomes six, and we can solve this 3-d SVAR system whose covariance matrix gives us six components. Since the residuals in 3-d system are disturbed by a new noise variable, the estimated parameters are changed from those of 2-d system. By finding 3-d system depending on a perturbed new noise variable whose black line is very close to that of the original 2-d system, we can estimate the true set of parameters as an intersection point of two black lines. Simulation experiments support the utility of our estimation method.
