Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity

Helmut Lütkepohl and Tomasz Woźniak

Journal of Economic Dynamics and Control, 2020, vol. 113, issue C

Abstract: In this study, Bayesian inference is developed for structural vector autoregressive models in which the structural parameters are identified via Markov-switching heteroskedasticity. In such a model, restrictions that are just-identifying in the homoskedastic case, become over-identifying and can be tested. A set of parametric restrictions is derived under which the structural matrix is globally or partially identified and a Savage–Dickey density ratio is used to assess the validity of the identification conditions. The latter is facilitated by analytical derivations that make the computations feasible and numerical standard errors small. As an empirical example, monetary models are compared using heteroskedasticity as an additional device for identification. The empirical results support an identified interest rate reaction function with money.

Keywords: Divisia money; Identification through heteroskedasticity; Monetary policy rules; Savage–Dickey density ratio (search for similar items in EconPapers)
JEL-codes: C11 C12 C32 E52 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity (2018)
Working Paper: Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:113:y:2020:i:c:s0165188920300324

DOI: 10.1016/j.jedc.2020.103862

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