A novel numerical method for the estimation of large-scale time-varying parameter seemingly unrelated regressions (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-SUR 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.

提出了一种用于估计大规模时变参数看似无关回归（TVP-SUR）模型的新数值方法。TVP-SUR 模型的更新和平滑估计是在广义线性最小二乘法的背景下以及通过允许模型的顺序估计的数值稳定的正交变换得出的。开发的方法基于计算有效的策略。通过利用 TVP-SUR 模型的特殊稀疏结构和利用先前的计算来降低计算成本。所提出的方法还扩展到TVP-SUR模型的滚动窗口估计。实验结果表明了新更新的有效性，