论文标题
在实时条件下使用贝叶斯矢量自动加工的预测
Forecasts with Bayesian vector autoregressions under real time conditions
论文作者
论文摘要
本文研究了预测性能指标对实时与伪外视角的敏感性。我们为美国和欧元区（EA）使用每月的年份，并估计具有恒定和时变参数（TVPS）和随机波动率（SV）的不同大小的矢量自回旋（VAR）模型。我们的结果表明，根据实时数据或伪样本中的最终年份截断，用于评估预测的实时数据还是截断的最终年份，用于点和密度预测的相对顺序差异。没有明显的跨变量类型和EA的较高规范可以识别，尽管具有TVP的较大型号似乎受到丢失的值和数据修订的影响，但较大的型号似乎受到了最少的影响。我们确定了在美国还是EA产生的预测方面的性能指标上存在实质性差异。
This paper investigates the sensitivity of forecast performance measures to taking a real time versus pseudo out-of-sample perspective. We use monthly vintages for the United States (US) and the Euro Area (EA) and estimate a set of vector autoregressive (VAR) models of different sizes with constant and time-varying parameters (TVPs) and stochastic volatility (SV). Our results suggest differences in the relative ordering of model performance for point and density forecasts depending on whether real time data or truncated final vintages in pseudo out-of-sample simulations are used for evaluating forecasts. No clearly superior specification for the US or the EA across variable types and forecast horizons can be identified, although larger models featuring TVPs appear to be affected the least by missing values and data revisions. We identify substantial differences in performance metrics with respect to whether forecasts are produced for the US or the EA.
