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Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks

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  • Savi Virolainen

Abstract

A structural Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the reduced form error covariance matrices with constraints on the time-varying impact matrix. This leads to flexible identification conditions, and some of the constraints are also testable. The empirical application studies asymmetries in the effects of the U.S. monetary policy shock and finds strong asymmetries with respect to the sign and size of the shock and to the initial state of the economy. The accompanying CRAN distributed R package gmvarkit provides a comprehensive set of tools for numerical analysis.

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  • Savi Virolainen, 2020. "Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks," Papers 2007.04713, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2007.04713
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    References listed on IDEAS

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    1. Jan Pablo Burgard & Matthias Neuenkirch & Matthias Nöckel, 2019. "State‐Dependent Transmission of Monetary Policy in the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(7), pages 2053-2070, October.
    2. Leena Kalliovirta & Mika Meitz & Pentti Saikkonen, 2015. "A Gaussian Mixture Autoregressive Model for Univariate Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 247-266, March.
    3. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, January.
    4. Kalliovirta, Leena & Meitz, Mika & Saikkonen, Pentti, 2016. "Gaussian mixture vector autoregression," Journal of Econometrics, Elsevier, vol. 192(2), pages 485-498.
    5. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    6. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    7. Savi Virolainen, 2020. "A mixture autoregressive model based on Gaussian and Student's $t$-distributions," Papers 2003.05221, arXiv.org, revised May 2020.
    8. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
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