论文标题
高维数据的时变预测组合
Time-varying Forecast Combination for High-Dimensional Data
论文作者
论文摘要
在本文中，我们提出了一个新的非参数估计量，这些估计值是随时间变化的预测组合权重。当单个预测的数量很少时，我们研究局部线性估计器的渐近性能。当候选人的预测数量超过样本量的差异或分歧时，我们会考虑以组SCAD惩罚的局部线性估计进行惩罚。我们表明，估算器具有Oracle属性，并正确选择了相关的预测，概率接近一个。模拟表明，当存在结构变化时，所提出的估计器的表现优于现有组合方案。关于通货膨胀预测和股票高级预测的两项实证研究突出了我们方法相对于其他流行方法的优点。
In this paper, we propose a new nonparametric estimator of time-varying forecast combination weights. When the number of individual forecasts is small, we study the asymptotic properties of the local linear estimator. When the number of candidate forecasts exceeds or diverges with the sample size, we consider penalized local linear estimation with the group SCAD penalty. We show that the estimator exhibits the oracle property and correctly selects relevant forecasts with probability approaching one. Simulations indicate that the proposed estimators outperform existing combination schemes when structural changes exist. Two empirical studies on inflation forecasting and equity premium prediction highlight the merits of our approach relative to other popular methods.
