Impulse Response Analysis of Structural Nonlinear Time Series Models

Abstract

This paper develops a semiparametric sieve approach to estimate impulse response functions of nonlinear time series models within a broad class of structural autoregressive specifications. A two-step procedure flexibly captures nonlinearities without imposing fixed parametric forms. We establish uniform estimation guarantees and propose an iterative algorithm that makes impulse response computation straightforward. Simulation results show robustness to misspecification with only modest efficiency losses. In an application to U.S. monetary policy, we find larger GDP responses to interest rate hikes than in linear models. We also examine oil supply news shocks of varying magnitudes to assess limitations when analyzing large shocks.