论文标题
使用图形拉索在结构中断下结合预测
Combining Forecasts under Structural Breaks Using Graphical LASSO
论文作者
论文摘要
在本文中，我们开发了一种新的方法，该方法基于一种称为图形套索（GL）的机器学习算法组合许多预测。我们将来自不同预报者的预测错误可视化，作为相互作用实体的网络，并在存在共同因素结构和结构断裂的情况下推广网络推断。首先，我们注意到预报员经常使用常见信息，因此会犯常见错误，这使得预测错误表现出共同的因素结构。我们使用因子图形套索（FGL，Lee和Seregina（2023））将公共预测误差与特质误差分开，并利用后者精确矩阵的稀疏性。其次，由于专家网络会随着时间的推移而变化，这是对不稳定环境（例如衰退）的响应，因此假设持续的预测组合权重是不合理的。因此，我们提出了依赖于制度的因子图形拉索（RD-FGL），该系数允许因子负载和特质精度矩阵依赖于制度。我们使用乘数的交替方向方法（ADMM）开发可扩展的实现，以估计与制度依赖性预测的组合权重。使用欧洲中央银行对专业预测者调查（ECB SPF）的数据进行预测宏观经济系列的经验应用，证明了使用FGL和RD-FGL的合并预测的出色性能。
In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). We visualize forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common mistakes, which makes the forecast errors exhibit common factor structures. We use the Factor Graphical LASSO (FGL, Lee and Seregina (2023)) to separate common forecast errors from the idiosyncratic errors and exploit sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments such as recessions, it is unreasonable to assume constant forecast combination weights. Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-FGL) that allows factor loadings and idiosyncratic precision matrix to be regime-dependent. We develop its scalable implementation using the Alternating Direction Method of Multipliers (ADMM) to estimate regime-dependent forecast combination weights. The empirical application to forecasting macroeconomic series using the data of the European Central Bank's Survey of Professional Forecasters (ECB SPF) demonstrates superior performance of a combined forecast using FGL and RD-FGL.
