我该留下还是离开？大规模混合新息模型的潜在阈值方法
Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models
摘要：
本文提出了一种直接的算法进行推断的大时变参数向量自回归（TVP-VAR）的混合新息分量的系统中的每个系数。我们显着降低了计算负担，通过近似的潜在指标，驱动系数的时间变化与潜在的阈值过程，取决于冲击的绝对大小。我们的方法的优点说明了两个应用程序。首先，我们预测了美国的利率期限结构，并证明了所提出的混合创新模型相对于其他基准模型的预测收益。其次，我们将我们的方法应用于美国宏观经济数据，并发现货币政策紧缩的时变效应的重要证据。
英文摘要：
This paper proposes a straightforward algorithm to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each coefficient in the system. We significantly decrease the computational burden by approximating the latent indicators that drive the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. The merits of our approach are illustrated with two applications. First, we forecast the US term structure of interest rates and demonstrate forecast gains of the proposed mixture innovation model relative to other benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
