Large Hybrid Time-Varying Parameter VARs
提出一种贝叶斯稀疏化方法，自动判断VAR模型中哪些方程的系数和同期关系是时变的、哪些是常数，从而构建混合时变参数VAR模型，并在美国多维度数据集上证明其预测优于多种标准基准。
Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification method for a class of models we call hybrid TVP-VARs—VARs with time-varying parameters in some equations but constant coefficients in others. Specifically, for each equation, the new method automatically decides whether the VAR coefficients and contemporaneous relations among variables are constant or time-varying. Using U.S. datasets of various dimensions, we find evidence that the parameters in some, but not all, equations are time varying. The large hybrid TVP-VAR also forecasts better than many standard benchmarks.