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Granger-causal-priority and choice of variables in vector autoregressions

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  • Jarociński, Marek
  • Maćkowiak, Bartosz

Abstract

A researcher is interested in a set of variables that he wants to model with a vector auto-regression and he has a dataset with more variables. Which variables from the dataset to include in the VAR, in addition to the variables of interest? This question arises in many applications of VARs, in prediction and impulse response analysis. We develop a Bayesian methodology to answer this question. We rely on the idea of Granger-causal-priority, related to the well-known concept of Granger-non-causality. The methodology is simple to use, because we provide closed-form expressions for the relevant posterior probabilities. Applying the methodology to the case when the variables of interest are output, the price level, and the short-term interest rate, we find remarkably similar results for the United States and the euro area. JEL Classification: C32, C52, E32

Suggested Citation

  • Jarociński, Marek & Maćkowiak, Bartosz, 2013. "Granger-causal-priority and choice of variables in vector autoregressions," Working Paper Series 1600, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20131600
    Note: 400529
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    5. 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).
    6. 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.
    7. 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.
    8. Al-Sadoon, Majid M., 2019. "Testing subspace Granger causality," Econometrics and Statistics, Elsevier, vol. 9(C), pages 42-61.
    9. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    10. Alonso, Pablo, 2018. "Creation and Evolution of Inflation Expectations in Paraguay," IDB Publications (Working Papers) 9027, Inter-American Development Bank.
    11. 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.
    12. 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.
    13. 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.

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    More about this item

    Keywords

    Bayesian model choice; granger-causal-priority; granger-noncausality; structural vector autoregression; vector autoregression;
    All these keywords.

    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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