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

TL;DR

This paper extends identification results for structural smooth transition VAR models, proposing new estimation methods and demonstrating their application in analyzing climate policy shocks and macroeconomic effects.

Contribution

It introduces a novel identification approach for time-varying impact matrices in SVAR models and provides an estimation strategy with an R package implementation.

Findings

Climate policy uncertainty shocks reduce production.

Such shocks increase inflation, especially during high uncertainty periods.

The methods improve understanding of macroeconomic responses to shocks.

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.