论文标题
神经菲利普斯曲线和深度输出间隙
A Neural Phillips Curve and a Deep Output Gap
论文作者
论文摘要
许多问题困扰经验菲利普斯曲线（PC）。其中两个关键组成部分，通货膨胀期望和输出差距都没有观察到的障碍。传统的疗法包括替代缺席者或通过假设较重的过滤程序提取它们。我提出了一条替代路线：半球神经网络（HNN），其结构产生了最终层，其中组件可以解释为神经PC中的潜在状态。有好处。首先，HNN进行了对非线性的监督估计，这些估计是在将一组观测到的回归器转换为潜在状态时出现的非线性估计。其次，预测在经济上是可解释的。除其他发现外，实际活动对通货膨胀的贡献似乎在传统的PC中被低估了。相比之下，HNN捕获了2021年的通货膨胀上升，并将其归因于从2020年底开始的巨大积极输出差距。HNN的差距的独特路径来自于失业和GDP分配，而有利于非线性处理的替代性紧密性指示器的合并。
Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. There are benefits. First, HNN conducts the supervised estimation of nonlinearities that arise when translating a high-dimensional set of observed regressors into latent states. Second, forecasts are economically interpretable. Among other findings, the contribution of real activity to inflation appears understated in traditional PCs. In contrast, HNN captures the 2021 upswing in inflation and attributes it to a large positive output gap starting from late 2020. The unique path of HNN's gap comes from dispensing with unemployment and GDP in favor of an amalgam of nonlinearly processed alternative tightness indicators.
