Data for: A new index of financial conditions

Abstract of associated article: We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the models׳ parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time. We discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables.关联论文摘要：我们采用带有时变系数与随机波动率（stochastic volatility）的因子增广向量自回归模型（factor augmented vector autoregressive models），构建可精准追踪美国核心宏观经济变量增长预期的金融状况指数（financial conditions index）。模型参数的时变特性允许该指数中各金融变量的权重随时间动态演进。此外，我们提出了适用于动态模型平均或选择（dynamic model averaging or selection）的方法，使纳入金融状况指数的金融变量可随时间调整。我们阐释了对现有文献进行此类拓展的重要意义，并通过涵盖多类金融变量的实证应用验证了这一点。