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

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  • Lütkepohl, Helmut
  • Woźniak, Tomasz

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.

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  • Lütkepohl, Helmut & Woźniak, Tomasz, 2020. "Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    • Handle: RePEc:eee:dyncon:v:113:y:2020:i:c:s0165188920300324
    DOI: 10.1016/j.jedc.2020.103862
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    3. Thiago Drummond de Mendonça Giudici & Elcyon Caiado Rocha Lima, 2024. "The business cycle in Brazil: identification via heteroskedasticity," International Economics and Economic Policy, Springer, vol. 21(3), pages 649-684, July.
    4. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    5. Jetro Anttonen & Markku Lanne & Jani Luoto, 2024. "Statistically identified structural VAR model with potentially skewed and fat‐tailed errors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 422-437, April.

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    More about this item

    Keywords

    Divisia money; Identification through heteroskedasticity; Monetary policy rules; Savage–Dickey density ratio;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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