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Targeting moments for calibration compared with indirect inference

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A common practice in estimating parameters in DSGE models is to find a set that when simulated gets close to an average of certain data moments; the model's simulated performance for other moments is then compared to the data for these as an informal test of the model. We call this procedure informal Indirect Inference, III. By contrast what we call Formal Indirect Inference, FII, chooses a set of moments as the auxiliary model and computes the Wald statistic for the joint distribution of these moments according to the structural DSGE model; it tests the model according to the probability of obtaining the data moments. The FII estimator then chooses structural parameters that maximise this probability. We show in this note via Monte Carlo experiments that the FII estimator has low bias in small samples, whereas the III estimator has much higher bias. It follows that models estimated by III will typically also be rejected by formal indirect inference tests.

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  • 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.
    • Handle: RePEc:cdf:wpaper:2022/12
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    1. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2017. "A Monte Carlo procedure for checking identification in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 202-210.
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    9. 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.
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    More about this item

    Keywords

    Moments; Indirect Inference;

    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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