Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265170 
Year of Publication: 
2022
Series/Report no.: 
Quaderni - Working Paper DSE No. 1171
Publisher: 
Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE), Bologna
Abstract: 
This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally. In this case there exists a set of isolated structural parameter points that are observationally equivalent under the imposed restrictions. Although the data do not inform us which observationally equivalent point should be selected, the common frequentist practice is to obtain one as a maximum likelihood estimate and perform impulse response analysis accordingly. For Bayesians, the lack of global identification translates to non-vanishing sensitivity of the posterior to the prior, and the multi-modal likelihood gives rise to computational challenges as posterior sampling algorithms can fail to explore all the modes. This paper overcomes these challenges by proposing novel estimation and inference procedures. We characterize a class of identifying restrictions that deliver local but non-global identification, and the resulting number of observationally equivalent parameter values. We propose algorithms to exhaustively compute all admissible structural parameters given reduced-form parameters and utilize them to sample from the multi-modal posterior. In addition, viewing the set of observationally equivalent parameter points as the identified set, we develop Bayesian and frequentist procedures for inference on the corresponding set of impulse responses. An empirical example illustrates our proposal.
Subjects: 
local identification
Bayesian inference
Markov Chain Monte Carlo
robust Bayesian inference
frequentist inference
multi-modal posterior
JEL: 
C01
C13
C30
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Working Paper

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