Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models
提出一种算法来估计大规模贝叶斯时变参数向量自回归模型，通过潜在阈值过程近似混合指标，降低计算负担。应用于美国利率期限结构预测和宏观经济数据，发现货币政策紧缩的时变效应。
Summary We propose a straightforward algorithm to estimate large Bayesian time‐varying parameter vector autoregressions with mixture innovation components for each coefficient in the system. The computational burden becomes manageable by approximating the mixture indicators driving the time‐variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. Two applications illustrate the merits of our approach. First, we forecast the US term structure of interest rates and demonstrate forecast gains relative to benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time‐varying effects of a monetary policy tightening.