Time Series Estimation of the Dynamic Effects of Disaster-Type Shock

TL;DR

This paper develops a novel SVAR framework accommodating disaster shocks with infinite variance, identifies these shocks via kurtosis, and proposes an independence test applicable to fat-tailed data, with applications to economic uncertainty and post-COVID shocks.

Contribution

It introduces a new SVAR methodology for infinite variance shocks, identifies disaster shocks through kurtosis, and offers a robust independence test for residuals in tail-heavy data.

Findings

Disaster shocks identified as high-kurtosis, negative-impact components.

The independence test works for fat and thin tails, and for over/just-identified models.

Disaster shocks influence economic fluctuations mainly through feedback mechanisms.

Abstract

This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but have non-standard asymptotics. Second, the disaster shock is identified as the component with the largest kurtosis and whose impact effect is negative. An estimator that is robust to infinite variance is used to recover the mutually independent components. Third, an independence test on the residuals pre-whitened by the Choleski decomposition is proposed to test the restrictions imposed on a SVAR. The test can be applied whether the data have fat or thin tails, and to over as well as exactly identified models. Three applications are considered. In the first, the independence test is used to shed light on the conflicting evidence regarding the role of uncertainty in economic fluctuations. In the second, disaster shocks are shown to have short term economic impact arising mostly from feedback dynamics. The third uses the framework to study the dynamic effects of economic shocks post-covid.