Refining set-identification in VARs through independence

Thorsten Drautzburg and Jonathan Wright

Journal of Econometrics, 2023, vol. 235, issue 2, 1827-1847

Abstract: Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help with identification; it is robust to weak identification. In simulations we show that it controls coverage well, in contrast to approaches that assume that the higher moments deliver point-identification. However, it requires large sample sizes and/or considerable non-normality to reduce the width of confidence intervals by much. We consider some empirical applications. We find that it can reject many possible rotations. The resulting confidence sets for impulse responses may be non-convex, corresponding to disjoint parts of the space of rotation matrices. We show that in this case, augmenting sign and magnitude restrictions with an independence requirement can yield bigger gains.

Keywords: Vector-autoregression; Sign restrictions; Set-identification; Weak identification; Independent shocks (search for similar items in EconPapers)
JEL-codes: C32 C51 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Refining Set-Identification in VARs through Independence (2021)
Working Paper: Refining Set-Identification in VARs through Independence (2021)
Working Paper: Refining Set-Identification in VARs through Independence (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1827-1847

DOI: 10.1016/j.jeconom.2023.01.011

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