Forecasting and stress testing with quantile vector autoregression
提出分位数向量自回归模型，能追踪不同分位数下内生变量的互动，用于欧元区实际与金融变量的压力测试，预测经济在遭受大冲击时的尾部行为。
Summary A quantile vector autoregressive (VAR) model, unlike standard VAR, traces the interaction among the endogenous random variables at any quantile. Quantile forecasts are obtained by factorizing the joint distribution in a recursive structure but cannot be obtained from reduced form estimation. Identification strategies and structural quantile impulse response functions are derived as generalization of the VAR model. The model is estimated using real and financial variables for the euro area. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks.