Identification of structural vector autoregressions through higher unconditional moments

This paper pursues two objectives. First, we determine the sufficient condition for local, statistical identification of SVAR processes through the third and fourth unconditional moments of the reduced-form innovations. Our findings provide novel insights when the entire system is not identified, as...

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Bibliographic Details
Published in:Journal of econometrics Vol. 225; no. 1; pp. 27 - 46
Main Author: Guay, Alain
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.11.2021
Elsevier Sequoia S.A
Subjects:
Bootstrap procedure
Econometrics
Estimating techniques
Excess kurtosis
Identification
Identification condition
Innovations
Kurtosis
Property
Rank test
Regression analysis
Skewness
statistical analysis
Structural vector autoregression
Symmetry
C32
Identification condition
C12
Skewness
Bootstrap procedure
Rank test
Structural vector autoregression
C51
Excess kurtosis
ISSN:0304-4076, 1872-6895
Online Access:Get full text
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Summary:This paper pursues two objectives. First, we determine the sufficient condition for local, statistical identification of SVAR processes through the third and fourth unconditional moments of the reduced-form innovations. Our findings provide novel insights when the entire system is not identified, as they highlight which subset of structural parameters is identified and which is not. Second, we elaborate a tractable testing procedure to verify whether the identification condition holds, prior to the estimation of the structural parameters of the SVAR process. To do so, we design a new bootstrap procedure that improves the small-sample properties of rank tests for the symmetry and kurtosis of the structural shocks.
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2020.10.006