Locally- but not globally-identified SVARs

Emanuele Bacchiocchi and Toru Kitagawa (t.kitagawa@ucl.ac.uk)
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Toru Kitagawa: Institute for Fiscal Studies and University College London

No CWP40/20, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 nonvanishing 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 parameter given reduced-form parameters and utilize them to sampling 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.

Date: 2020-07-27
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Locally- but not Globally-identified SVARs (2025)
Working Paper: Locally- but not Globally-identified SVARs (2022)
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