期刊名称：Cardiff Economics Working Papers / Cardiff University, Cardiff Business School
印刷版ISSN：1749-6101
出版年度：2022
期号：10
语种：English
出版社：Cardiff University
摘要：Maximum Likelihood (ML) shows both lower power and higher bias in small sample Monte Carlo experiments than Indirect Inference (II) and IIís higher power comes from its use of the model-restricted distribution of the auxiliary model coefficients (Le et al. 2016). We show here that IIís higher power causes it to have lower bias, because false parameter values are rejected more frequently under II; this greater rejection frequency is partly offset by a lower tendency for ML to choose unrejected false parameters as estimates, due again to its lower power allowing greater competition from rival unrejected parameter sets.
