Statistics and Data Science Seminar: "Time Series Estimation of the Dynamic Effects of Disaster-Type Shocks"

Speaker: Richard Davis, Columbia University

Abstract: The 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 VAR. We show that the least squares estimates of the VAR are consistent but have non-standard properties. 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 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 application uses this framework to study the dynamic effects of economic shocks post-covid.  (This is joint work with Serena Ng.)

Hosts: Likai Chen and Debashis Mondal