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Robust inference intime-varying structural VAR models: The DC-Cholesky multivariate stochasticvolatility model

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  • Hartwig, Benny

Abstract

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying. Simulations demonstrate that estimated co-variance matrices become more divergent when volatility clusters idiosyncratically.It is illustrated that this property is important for empirical applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy andinflation-gap persistence indicate that conclusions may critically hinge on a selectedordering of variables. The dynamic correlation Cholesky multivariate stochasticvolatility model is proposed as a robust alternative.

Suggested Citation

  • Hartwig, Benny, 2020. "Robust inference intime-varying structural VAR models: The DC-Cholesky multivariate stochasticvolatility model," Discussion Papers 34/2020, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:342020
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    References listed on IDEAS

    as
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    Cited by:

    1. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    2. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Shin, Minchul, 2023. "Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1054-1086.
    3. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    4. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.

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    More about this item

    Keywords

    Model uncertainty; Multivariate stochastic volatility; Dynamic correlations; Monetary policy; Structural VAR;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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