B0188
Title: Robust inference for non-Gaussian SVAR models
Authors: Adam Lee - BI Norwegian Business School (Norway) [presenting]
Geert Mesters - Universitat Pompeu Fabra (Spain)
Lukas Hoesch - Vrije Universiteit Amsterdam (Netherlands)
Abstract: All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, a robust semi-parametric approach is proposed to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non-Gaussianity when it is present, but yields correct size/coverage regardless of the distance to the Gaussian distribution. Empirically, two macroeconomic SVAR studies are revisited: these exercises highlight the importance of using weak identification robust methods to asses estimation uncertainty when using non-Gaussianity for identification.
