Dealing with misspecification in structural macroeconometric models

• 成果类型: Article
• 署名作者: Canova, Fabio; Matthes, Christian
• 署名单位: BI Norwegian Business School; Centre for Economic Policy Research - UK; Indiana University System; Indiana University Bloomington
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1413
• 发表日期: 2021
• 页码: 313-350
• 关键词: Model misspecification; composite likelihood; Bayesian model averaging; finite mixture; C13; C51; E17

摘要

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood-based estimators in mean squared error and composite models are superior to individual models in the Kullback-Leibler sense. We describe Bayesian quasi-posterior computations and compare our approach to Bayesian model averaging, finite mixture, and robust control procedures. We robustify inference using the composite posterior distribution of the parameters and the pool of models. We provide estimates of the marginal propensity to consume and evaluate the role of technology shocks for output fluctuations.