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Granger-Causal-Priority and Choice of Variables in Vector Autoregressions

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  • Bartosz Mackowiak

    (European Central Bank)

Abstract

We derive a closed-form expression for the posterior probability of Granger-noncausality in a Gaussian vector autoregression with a conjugate prior. We also express in closed form the posterior probability of Granger-causal-priority, a more general relation that accounts for indirect effects between variables and therefore is suitable in a multivariate context. We show how to use these results to choose variables for a vector autoregression, whether the goal is prediction or impulse response analysis.

Suggested Citation

  • Bartosz Mackowiak, 2015. "Granger-Causal-Priority and Choice of Variables in Vector Autoregressions," 2015 Meeting Papers 66, Society for Economic Dynamics.
    • Handle: RePEc:red:sed015:66
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    Cited by:

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    2. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    3. Woźniak, Tomasz, 2015. "Testing causality between two vectors in multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 876-894.
    4. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    5. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    6. Alonso, Pablo, 2018. "Creation and Evolution of Inflation Expectations in Paraguay," IDB Publications (Working Papers) 9027, Inter-American Development Bank.
    7. James Morley & Benjamin Wong, 2020. "Estimating and accounting for the output gap with large Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 1-18, January.
    8. Donal Smith, 2016. "The International Impact of Financial Shocks: A Global VAR and Connectedness Measures Approach," Discussion Papers 16/07, Department of Economics, University of York.
    9. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    10. Matthieu Droumaguet & Anders Warne & Tomasz Wozniak, 2015. "Granger Causality and Regime Inference in Bayesian Markov-Switching VARs," Department of Economics - Working Papers Series 1191, The University of Melbourne.
    11. Zhang, Ailian & Pan, Mengmeng & Liu, Bai & Weng, Yin-Che, 2020. "Systemic risk: The coordination of macroprudential and monetary policies in China," Economic Modelling, Elsevier, vol. 93(C), pages 415-429.
    12. Manfred Kremer, 2016. "Macroeconomic effects of financial stress and the role of monetary policy: a VAR analysis for the euro area," International Economics and Economic Policy, Springer, vol. 13(1), pages 105-138, January.
    13. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.

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    More about this item

    JEL classification:

    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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