Why does Indirect Inference estimation produce less small sample bias than maximum likelihood? A note

David Meenagh, A. Patrick Minford and Yongdeng Xu

No E2022/10, Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section

Abstract: Maximum Likelihood (ML) shows both lower power and higher bias in small sample Monte Carlo experiments than Indirect Inference (II) and II s higher power comes from its use of the model-restricted distribution of the auxiliary model coefficients (Le et al. 2016). We show here that II s 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.

Keywords: Bias; Indirect Inference; Maximum Likelihood (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 (search for similar items in EconPapers)
Pages: 8 pages
Date: 2022-05
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (4)

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