Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2022-11-13
Number: 22-080/III
Author-Name: Lukas Hoesch
Author-Workplace-Name: Vrije Universiteit Amsterdam 
Author-Name: Adam Lee
Author-Workplace-Name: BI Norwegian Business School

Author-Name: Geert Mesters
Author-Workplace-Name: Universitat Pompeu Fabra
Title: Robust Inference for Non-Gaussian SVAR models
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, we propose a robust semi-parametric approach
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
we revisit two macroeconomic SVAR studies where we document mixed results. For the oil
price model of Kilian and Murphy (2012) we find that non-Gaussianity can robustly identify
reasonable confidence sets, whereas for the labour supply-demand model of Baumeister and
Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance
of using weak identification robust methods to assess estimation uncertainty when using
non-Gaussianity for identification.
Classification-JEL: C32, C39, C51

Keywords: weak identification, semi-parametric inference, hypothesis testing, impulse responses, independent component analysis
File-URL: https://papers.tinbergen.nl/22080.pdf
File-Format: application/pdf
File-Size: 1.576.192  bytes
Handle: RePEc:tin:wpaper:20220080