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

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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.

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  • Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2022. "Why does Indirect Inference estimation produce less small sample bias than maximum likelihood? A note," Cardiff Economics Working Papers E2022/10, Cardiff University, Cardiff Business School, Economics Section.
    • Handle: RePEc:cdf:wpaper:2022/10
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    1. Vo Le & David Meenagh & Patrick Minford & Michael Wickens & Yongdeng Xu, 2016. "Testing Macro Models by Indirect Inference: A Survey for Users," Open Economies Review, Springer, vol. 27(1), pages 1-38, February.
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    1. David Meenagh & Patrick Minford & Yongdeng Xu, 2024. "Indirect Inference and Small Sample Bias — Some Recent Results," Open Economies Review, Springer, vol. 35(2), pages 245-259, April.
    2. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2022. "Targeting moments for calibration compared with indirect inference," Cardiff Economics Working Papers E2022/12, Cardiff University, Cardiff Business School, Economics Section.
    3. Minford, Patrick & Xu, Yongdeng, 2024. "Indirect Inference- a methodological essay on its role and applications," Cardiff Economics Working Papers E2024/1, Cardiff University, Cardiff Business School, Economics Section.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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