Federal Reserve Bank of Atlanta
FRB Atlanta Working Paper
Inference in Bayesian Proxy-SVARs
Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy-SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often imposed to facilitate inference when more than one instrument are used to identify more than one equation, as in Mertens and Montiel-Olea (2018).
Cite this item
Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, Inference in Bayesian Proxy-SVARs, Federal Reserve Bank of Atlanta, FRB Atlanta Working Paper 2018-16, 01 Dec 2018.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Keywords: SVARs; external instruments; importance sampler
This item with handle RePEc:fip:fedawp:2018-16
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