🤖 AI Summary
This paper addresses the statistical identifiability of structural thresholds and smooth-transition VAR models under shock independence and at most one Gaussian shock. Methodologically, it introduces the first extension of independent non-Gaussian identification to a nonlinear SVAR framework featuring time-varying structural matrices, thereby transcending conventional zero-restriction reliance. Identification is achieved globally solely through shock non-Gaussianity, resolving shock-label ambiguity across regimes. The approach integrates independent component analysis, non-Gaussianity testing, and logistic smooth-transition mechanisms, with implementation supported by the R package *sstvars*. Empirically, the framework reveals that climate policy uncertainty shocks exert persistent inflationary effects—significantly amplified during periods of high economic policy uncertainty—and robustly suppress output.
📝 Abstract
Linear structural vector autoregressive models can be identified statistically without imposing restrictions on the model if the shocks are mutually independent and at most one of them is Gaussian. We show that this result extends to structural threshold and smooth transition vector autoregressive models incorporating a time-varying impact matrix defined as a weighted sum of the impact matrices of the regimes. We also discuss the problem of labelling the shocks, estimation of the parameters, and stationarity the model. The introduced methods are implemented to the accompanying R package sstvars. Our empirical application studies the effects of the climate policy uncertainty shock on the U.S. macroeconomy. In a structural logistic smooth transition vector autoregressive model with two regimes, we find that a positive climate policy uncertainty shock decreases production and increases inflation in times of both low and high economic policy uncertainty, but its inflationary effects are stronger in the periods of high economic policy uncertainty.