Quantitative Economics

Journal Of The Econometric Society

Edited by: Stéphane Bonhomme • Print ISSN: 1759-7323 • Online ISSN: 1759-7331

Quantitative Economics: May, 2024, Volume 15, Issue 2

Locally Robust Inference for Non-Gaussian SVAR models

p. 523-570

Lukas Hoesch, Adam Lee, Geert Mesters

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 locally robust semiparametric 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 for local‐to‐Gaussian densities. 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 labor 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.

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Supplemental Material

Supplement to "Locally Robust Inference for Non-Gaussian SVAR models"

Lukas Hoesch, Adam Lee, and Geert Mesters

The replication package for this paper is available at https://doi.org/10.5281/zenodo.10539210. The Journal checked the data and codes included in the package for their ability to reproduce the results in the paper and approved online appendices.

Supplement to "Locally Robust Inference for Non-Gaussian SVAR models"

Lukas Hoesch, Adam Lee and Geert Mesters

In this supplementary material we provide the following additional results.
S1: Choice for the parametrization
S2: Technical details for the main proofs
S3: Some technical tools

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