Conditional Forecasts in Dynamic Multivariate Models
开发了贝叶斯方法，用于计算向量自回归模型中条件预测的精确有限样本分布，并考虑了参数不确定性，适用于结构式和简化式VAR模型。
In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions. This paper develops Bayesian methods for computing the exact finite-sample distribution of conditional forecasts. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for parameter uncertainty in finite samples. Empirical examples under both a flat prior and a reference prior are provided to show the use of these methods. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology