Macro-Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models

• 成果类型: Article
• 署名作者: Cheng, Xu; Dou, Winston Wei; Liao, Zhipeng
• 署名单位: University of Pennsylvania; University of Pennsylvania; University of California System; University of California Los Angeles
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA18506
• 发表日期: 2022
• 页码: 685-713
• 关键词: Long-run risks; rare disasters; generalized-method; stock-market; sample properties; weak instruments; ANDERSON-RUBIN; inference; tests; gmm

摘要

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time-varying) rare-disaster risk models and long-run risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a measure capturing the macroeconomic information decoupled from the underlying content of asset pricing theories. Macro-finance decoupling is an effective way to improve the power of the specification test when asset pricing theories are difficult to refute because of a severe imbalance in the information content about the key model parameters between macroeconomic moment restrictions and asset pricing cross-equation restrictions. We apply the proposed conditional specification test to the evaluation of a time-varying rare-disaster risk model and the construction of robust model uncertainty sets.