Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity

Helmut Lütkepohl and Tomasz Woźniak

No 1707, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: In order to identify structural shocks that affect economic variables, restrictions need to be imposed on the parameters of structural vector autoregressive (SVAR) models. Economic theory is the primary source of such restrictions. However, only over-identifying restrictions can be tested with statistical methods which limits the statistical validation of many just-identified SVAR models. In this study, Bayesian inference is developed for SVAR 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 identified and a Savage-Dickey density ratio is used to assess the validity of the identification conditions. For that purpose, a new probability distribution is defined that generalizes the beta, F, and compound gamma distributions. As an empirical example, monetary models are compared using heteroskedasticity as an additional device for identification. The empirical results support models with money in the interest rate reaction function.

Keywords: Identification through heteroskedasticity; Markov-Switching models; Savage-Dickey Density Ratio; monetary policy shocks; Divisia Money (search for similar items in EconPapers)
JEL-codes: C11 C12 C32 E32 (search for similar items in EconPapers)
Pages: 37 p.
Date: 2017
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
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Related works:
Journal Article: Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity (2020)
Working Paper: Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity (2018)
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