Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/200236 
Year of Publication: 
2019
Series/Report no.: 
cege Discussion Papers No. 375
Publisher: 
University of Göttingen, Center for European, Governance and Economic Development Research (cege), Göttingen
Abstract: 
Structural vector autoregressive analysis aims to trace the contemporaneous linkages among (macroeconomic) variables back to underlying orthogonal structural shocks. In homoskedastic Gaussian models the identification of these linkages deserves external and typically notdata-based information. Statistical data characteristics (e.g, heteroskedasticity or non-Gaussian independent components) allow for unique identification. Studying distinct covariance changes and distributional frameworks, we compare alternative data-driven identification procedures and identification by means of sign restrictions. The application of sign restrictions results in estimation biases as a reflection of censored sampling from a space of covariance decompositions. Statistical identification schemes are robust under distinct data structures to some extent. The detection of independent components appears most flexible unless the underlying shocks are (close to) Gaussianity. For analyzing linkages among the US business cycle and distinct sources of uncertainty we benefit from simulation-based evidence to point at two most suitable identification schemes. We detect a unidirectional effect of financial uncertainty on real economic activity and mutual causality between macroeconomic uncertainty and business cycles.
Subjects: 
independent components
heteroskedasticity
model selection
non-Gaussianity
structural shocks
JEL: 
C32
E00
E32
E44
G01
Document Type: 
Working Paper

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