论文标题
大型混合时间变化参数vars
Large Hybrid Time-Varying Parameter VARs
论文作者
论文摘要
具有随机波动率的时变参数VAR通常用于结构分析和预测，并在涉及一些内源变量的设置中进行预测。由于密集的计算和过度参数的问题，将这些模型应用于高维数据集已被证明是具有挑战性的。我们为一类模型开发了一种有效的贝叶斯稀疏方法，我们称为混合tvp-vars-在某些方程式中具有随时间变化的参数，但在其他方程式中具有恒定的系数。具体而言，对于每个方程式，新方法自动决定变量之间的VAR系数和同时关系是恒定还是随时间变化。使用各个维度的我们的数据集，我们发现证据表明某些（但不是全部）的参数在变化。大型混合动力TVP-VAR也可以比许多标准基准测试更好。
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 US 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.
