The random coefficient state-space model was first introduced by McKenzie and Gardner (2010). This model is a stochastic combination of simple and double exponential smoothing, a desirable feature for time-series forecasting. This paper provides a simple method to estimate the random coefficient state-space model parameters by exploiting the link between the model’s autocovariance and the Kalman filter. A simulation exercise shows that the proposed estimator has good finite-sample properties. This paper also evaluates the model’s forecasting performance in large-scale empirical applications, which is remarkable. Indeed, this model outperforms all competing (not-combined) benchmarks when using the yearly data from the M3 competition dataset. Furthermore, employing the yearly data from the M4 competition, it continues to beat its competitors, with a performance comparable to that of the Theta method. The predictive performance is assessed using both the MASE/sMAPE metrics and the Model Confidence Set procedure.

随机系数状态空间模型首先由 McKenzie 和 Gardner (2010) 引入。该模型是简单和双指数平滑的随机组合，是时间序列预测的理想特征。本文提供了一种通过利用模型的自协方差和卡尔曼滤波器之间的联系来估计随机系数状态空间模型参数的简单方法。模拟练习表明，所提出的估计器具有良好的有限样本特性。本文还评估了该模型在大规模实证应用中的预测性能，可圈可点。事实上，当使用来自 M3 竞赛数据集的年度数据时，该模型优于所有竞争（非组合）基准。此外，利用 M4 比赛的年度数据，它继续击败竞争对手，性能可与 Theta 方法相媲美。使用 MASE/sMAPE 指标和模型置信集程序评估预测性能。