We propose a focused weighted-average least squares (FWALS) estimator that addresses the computational burden of focused model averaging. By semi-orthogonalizing auxiliary regressors, the weighting problem is reduced from $2^{k_2}$ sub-models to at most $k_2$ regressor-wise weights, yielding a tractable sub-optimal procedure. Under local-to-zero conditions, we derive the limiting distribution of FWALS for smooth focused functions and provide a plug-in AMSE criterion for data-driven weight selection. Simulations show that FWALS closely matches the focused information criterion (FIC) benchmark and delivers stable performance when focused function is designed for impulse response function. Prior-based WALS can be competitive in some settings, but its performance depends on the signal regime and the design of focused parameter. Overall, FWALS offers a practical and robust alternative with substantial computational savings.

翻译：本文提出一种聚焦加权平均最小二乘（FWALS）估计器，以应对聚焦模型平均中的计算负担。通过对辅助回归变量进行半正交化处理，加权问题从 $2^{k_2}$ 个子模型简化为至多 $k_2$ 个回归变量层面的权重，从而得到一种易于处理的次优方法。在局部趋近于零的条件下，我们推导了FWALS对于光滑聚焦函数的极限分布，并提出了一种用于数据驱动权重选择的插件式渐近均方误差准则。仿真结果表明，当聚焦函数针对脉冲响应函数设计时，FWALS与聚焦信息准则（FIC）基准高度吻合，并展现出稳定的性能。基于先验的WALS在某些设定下可能具有竞争力，但其表现依赖于信号机制与聚焦参数的设计。总体而言，FWALS在显著节省计算成本的同时，提供了一种实用且稳健的替代方案。