Finite‐Sample Identification‐Robust Inference for Nonlinear DSGE Models
提出一种不依赖线性近似的识别稳健置信集方法，用于非线性DSGE模型，通过模拟和实证验证了不对称冲击的信息价值。
ABSTRACT We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projections. Proposed procedures are illustrated through laboratory experiments and an empirical application on a nonlinear real business cycle model. In simulations, we study size, power, and the trade‐off between robustness and insensitivity to misspecification. Empirically, results underscore the information content of asymmetric shocks and the identification gains on impulse responses.