Identification by non-Gaussianity in structural smooth transition vector autoregressive models
Abstract
We show that structural smooth transition vector autoregressive models are statistically identified if the shocks are mutually independent and at most one of them is Gaussian. This extends a known identification result for linear structural vector autoregressions to a time-varying impact matrix. We also propose an estimation method, show how a blended identification strategy can be adopted to address weak identification, and establish a sufficient condition for ergodic stationarity. The introduced methods are implemented in the accompanying R package sstvars. Our empirical application finds that a positive climate policy uncertainty shock reduces production and raises inflation under both low and high economic policy uncertainty, but its effects, particularly on inflation, are stronger during the latter.
Fields of Study & Subjects
Neural Networks and Applications
Control Systems and Identification
Publication details
- Journal / source
- Econometric Reviews
- Publisher
- Informa UK Limited
- Volume
- 45
- Issue
- 8
- Pages
- 1230–1248
- Publication date
- 29 May 2026
- Language
- EN