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Finite‐Sample Identification‐Robust Inference for Nonlinear DSGE Models

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Our method produces confidence sets for nonlinear DSGE models, highlighting the impact of auxiliary statistics and robustness.

Robust simultaneous confidence sets are developed for nonlinear dsge models without linear approximations.
Results emphasize the trade-off between robustness and insensitivity to model misspecification in simulations.
The methodology combines simulation estimation methods with Monte Carlo principles for deeper insights.
Applications show how asymmetric shocks provide greater identification gains on impulse responses in economic models.

Khalaf et al. (2025) studied this question.

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