EconPapers    
Economics at your fingertips  
 

A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments

Sascha Alexander Keweloh

Journal of Business & Economic Statistics, 2021, vol. 39, issue 3, 772-782

Abstract: I propose a generalized method of moments estimator for structural vector autoregressions with independent and non-Gaussian shocks. The shocks are identified by exploiting information contained in higher moments of the data. Extending the standard identification approach, which relies on the covariance, to the coskewness and cokurtosis allows the simultaneous interaction to be identified and estimated without any further restrictions. I analyze the finite sample properties of the estimator and apply it to illustrate the simultaneous interaction between economic activity, oil, and stock prices. Supplementary materials for this article are available online.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2020.1730858 (text/html)
Access to full text is restricted to subscribers.

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:taf:jnlbes:v:39:y:2021:i:3:p:772-782

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2020.1730858

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-27
Handle: RePEc:taf:jnlbes:v:39:y:2021:i:3:p:772-782