Identification of structural vector autoregressions through higher unconditional moments

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Document de travail ; No. 2020-14

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation
• Subject: Bootstrap procedure ; excess kurtosis ; identification condition ; rank test ; skewness ; structural vector autoregression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Guay, Alain
• Event: Veröffentlichung
• (who): Université du Québec à Montréal, École des sciences de la gestion (ESG UQAM), Département des sciences économiques
• (where): Montréal
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/234814
• Last update: 10.03.2025, 11:41 AM CET