论文标题
大型时变参数模型的柔性混合先验
Flexible Mixture Priors for Large Time-varying Parameter Models
论文作者
论文摘要
时变参数（TVP）模型通常假定TVP根据随机步行而演变。但是，此假设可能是值得怀疑的，因为它意味着系数以无限的方式变化平稳。在本文中，我们通过提出在大规模矢量自动加注（VAR）中提出灵活的运动定律来放松这一假设。我们没有在状态方程的系数上仔细设计层次的混合先验，而不是施加潜在状态的限制性随机行走演变。这些先验有效地允许区分系数根据随机步行和TVP更好地以固定随机过程为特征的时期演变的时期。此外，这种方法能够通过在必要时将小参数更改推向零来引入动态稀疏性。模型的优点是通过两个应用程序说明的。使用合成数据，我们表明我们的方法得出精确的参数估计值。当应用于美国数据时，该模型揭示了系数低频动力学的有趣模式，相对于广泛的竞争模型很好。
Time-varying parameter (TVP) models often assume that the TVPs evolve according to a random walk. This assumption, however, might be questionable since it implies that coefficients change smoothly and in an unbounded manner. In this paper, we relax this assumption by proposing a flexible law of motion for the TVPs in large-scale vector autoregressions (VARs). Instead of imposing a restrictive random walk evolution of the latent states, we carefully design hierarchical mixture priors on the coefficients in the state equation. These priors effectively allow for discriminating between periods where coefficients evolve according to a random walk and times where the TVPs are better characterized by a stationary stochastic process. Moreover, this approach is capable of introducing dynamic sparsity by pushing small parameter changes towards zero if necessary. The merits of the model are illustrated by means of two applications. Using synthetic data we show that our approach yields precise parameter estimates. When applied to US data, the model reveals interesting patterns of low-frequency dynamics in coefficients and forecasts well relative to a wide range of competing models.
