Conditional forecasts in dynamic multivariate models

• 成果类型: Article; Proceedings Paper
• 署名作者: Waggoner, DF; Zha, T
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - Atlanta
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/003465399558508
• 发表日期: 1999-11
• 页码: 639-651
• 关键词: simulation

摘要

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.