Robust inference for non-Gaussian SVAR models

Lukas Hoesch (), Adam Lee and Geert Mesters

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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 asses estimation uncertainty when using non-Gaussianity for identification.

Keywords: weak identification; semi-parametric inference; hypothesis testing; impulse responses; independent component analysis (search for similar items in EconPapers)
JEL-codes: C32 C39 C51 (search for similar items in EconPapers)
Date: 2022-10
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://econ-papers.upf.edu/papers/1847.pdf Whole Paper (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:1847

Access Statistics for this paper

More papers in Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra
Bibliographic data for series maintained by ( this e-mail address is bad, please contact ).