This paper proposes a new Bayesian machine learning model that can be applied to large datasets arising in macroeconomics. Our framework sums over many simple two-component location mixtures. The transition between components is determined by a logistic function that depends on a single threshold variable and two hyperparameters. Each of these individual models only accounts for a minor portion of the variation in the endogenous variables. But many of them are capable of capturing arbitrary nonlinear conditional mean relations. Conjugate priors enable fast and efficient inference. In simulations, we show that our approach produces accurate point and density forecasts. In a real-data exercise, we forecast US macroeconomic aggregates and consider the nonlinear effects of financial shocks in a large-scale nonlinear VAR.

翻译：本文提出一种新的贝叶斯机器学习模型，可应用于宏观经济学中的大规模数据集。该框架对大量简单的两分量位置混合模型进行求和。分量之间的转换由一个逻辑函数决定，该函数依赖于单个阈值变量和两个超参数。这些个体模型中的每一个仅解释内生变量变异的较小部分；但大量这样的模型能够捕捉任意非线性条件均值关系。共轭先验使得快速有效的推理成为可能。在模拟研究中，我们证明该方法能够生成精确的点预测和密度预测。在真实数据实验中，我们预测了美国宏观经济总量，并在大规模非线性向量自回归模型中考虑了金融冲击的非线性效应。