Double robust inference for continuous updating GMM

• 成果类型: Article
• 署名作者: Kleibergen, Frank; Zhan, Zhaoguo
• 署名单位: University of Amsterdam; University System of Georgia; Kennesaw State University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2347
• 发表日期: 2025
• 页码: 295-327
• 关键词: Weak identification; misspecification; Robust Inference; Lagrange multiplier; C12; C18; G12

摘要

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the minimizer of the population continuous updating objective function. The (bounding) chi 2 limiting distribution of the DRLM statistic is robust to both misspecification and weak identification, hence its name. The minimizer is the so-called pseudo-true value, which equals the true value of the structural parameter under correct specification. To emphasize its importance for applied work where misspecification and weak identification are common, we use the DRLM test to analyze: the risk premia in Adrian et al. (2014) and He et al. (2017); the structural parameters in a nonlinear asset pricing model with constant relative risk aversion.