Deterministic regression smoothness priors TVAR modelling

In this paper we propose a method for the estimation of time-varying autoregressive (TVAR) processes. The approach is essentially to regularize the heavily underdetermined unconstrained prediction equations with a smoothness priors type side constraint. The implementation of nonhomogeneous smoothness properties is straightforward. The method is compared to the usual deterministic regression approach (TVAR) in which the coefficient evolutions are constrained to a subspace. It is shown that the typical transient oscillations of TVAR can be avoided with the proposed method