An alternative numerical method for estimating large-scale time-varying parameter seemingly unrelated regressions models

A novel numerical method for the estimation of large-scale time-varying parameter seemingly unrelated regression (TVP-SUR) models is proposed. The updating and smoothing estimates of the TVP-SUR model are derived within the context of generalised linear least squares and through numerically stable orthogonal transformations which allow the sequential estimation of the model. The method developed is based on computationally efficient strategies. The computational cost is reduced by exploiting the special sparse structure of the TVP-SUR model and by utilising previous computations. The proposed method is also extended to the rolling window estimation of the TVP model. Experimental results show the effectiveness of the new updating, rolling window and smoothing strategies in high dimensions when a large number of covariates and regressions are included in the TVP-SUR model, and in the presence of an ill-conditioned data matrix.