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多国环境下的宏观经济预测AI 解读

Abstract In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe…

非凸损失下经验风险的几何结构AI 解读

Most high-dimensional estimation methods propose to minimize a cost function (empirical risk) that is a sum of losses associated to each data point (each example). In this paper, we focus on the case of nonconvex losses. Classical empirical process theory implies uniform convergence of the empirical (or sample) risk to the population risk. While under additional assumptions, uniform convergence implies consistency of the resulting M-estimator, it does not ensure that the…