Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models

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.