Prediction Using Several Macroeconomic Models

• 成果类型: Article
• 署名作者: Amisano, Gianni; Geweke, John
• 署名单位: Federal Reserve System - USA; Federal Reserve System Board of Governors; University of Washington; University of Washington Seattle
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00655
• 发表日期: 2017-12
• 页码: 912-925
• 关键词: vector autoregressions; monetary-policy

摘要

We establish methods that improve the predictions of macroeconometric modelsdynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressionsusing a quarterly U.S. data set. We measure prediction quality with one-step-ahead probability densities assigned in real time. Two steps lead to substantial improvements: (a) the use of full Bayesian predictive distributions rather than conditioning on the posterior mode for parameters and (b) the use of an equally weighted pool.