arXiv 3 Aug 2026 · Econometrics
arXiv:2608.02943 · PDF · Extracted main text
Recursive nonlinear impulse responses require an estimated innovation law whenever the impact shock is normalized by innovation ranks and future innovations are integrated out. The closest semiparametric recursive construction in the literature estimates the relevant innovation quantile functions smoothly and discusses a direct empirical-residual implementation without developing its complete first-order inference theory. We tackle this gap in a finite-dimensional nonlinear structural autoregression with unrestricted continuous marginal innovation distributions and a fixed normal-rank shock. Our estimator replaces each innovation quantile function with the empirical quantile of generated structural residuals and iterates the same structural transition. For any fixed collection of responses, we establish a joint \sqrt{T} asymptotic linear representation with four components: direct transition estimation, the effect of transition estimation on residual order statistics, ordinary innovation-quantile estimation, and the shifted impact quantile. After projection through the recursion, the quantile terms admit a residual-rank-and-spacing representation, yielding feasible inference without innovation-density estimation or quantile smoothing. We then characterize the propagated bias from smoothing, establish validity of a full recursive residual bootstrap, and derive the additional covariance contribution from a finite number of simulated paths, providing bandwidth-free inference for the empirical-residual version of the same normal-rank response used in the smooth recursive construction.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | van der Vaart, A. W. and Wellner, Jon A (2023) Weak Convergence and Empirical Processes: With Applications to Statistics | 0.550 | 6 | 4 | 17% |
| 2 | Beutner, Eric and Zähle, Henryk (2016) Functional Delta-Method for the Bootstrap of Quasi-Hadamard Differentiable Functionals | 0.511 | 2 | 2 | 50% |
| 3 | Gouriéroux, Christian and Lee, Q (2025) Nonlinear Impulse Response Functions and Local Projections | 0.511 | 2 | 1 | 100% |
| 4 | Ballarin, Giovanni (2025) Impulse Response Analysis of Structural Nonlinear Time Series Models | 0.405 | 1 | 1 | 100% |
| 5 | Gon calves, Sílvia and Herrera, Ana María and Kilian, Lutz and Pesav… (2024) Nonparametric Local Projections | 0.405 | 1 | 1 | 100% |
| 6 | Gon calves, Sílvia and Herrera, Ana María and Kilian, Lutz and Pesav… (2026) Semiparametric Local Projections | 0.405 | 1 | 1 | 100% |
| 7 | Jordà, Òscar (2005) Estimation and Inference of Impulse Responses by Local Projections | 0.405 | 1 | 1 | 100% |
| 8 | Jordà, Òscar and Taylor, Alan M (2025) Local Projections | 0.405 | 1 | 1 | 100% |
| 9 | Koop, Gary and Pesaran, M. Hashem and Potter, Simon M (1996) Impulse Response Analysis in Nonlinear Multivariate Models | 0.405 | 1 | 1 | 100% |
| 10 | Plagborg-Møller, Mikkel and Wolf, Christian K (2021) Local Projections and VARs Estimate the Same Impulse Responses | 0.405 | 1 | 1 | 100% |
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