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Noncausal Bayesian Vector Autoregression

Author

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  • Markku Lanne
  • Jani Luoto

Abstract

We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by-product. We apply the methods to postwar quarterly U.S. inflation and GDP growth series. The noncausal VAR model turns out to be superior in terms of both in-sample fit and out-of-sample forecasting performance over its conventional causal counterpart. In addition, we find GDP growth to have predictive power for the future distribution of inflation over and above the own history of inflation, but not vice versa. This may be interpreted as evidence against the new Keynesian model that implies Granger causality from inflation to GDP growth, provided GDP growth is a reasonable proxy of the marginal cost.
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Suggested Citation

  • Markku Lanne & Jani Luoto, 2016. "Noncausal Bayesian Vector Autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1392-1406, November.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:7:p:1392-1406
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    Cited by:

    1. Numan Ülkü & Kexing Wu, 2023. "Stock Market's Response to Real Output Shocks in China: A VARwAL Estimation," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 31(5), pages 1-25, September.
    2. Christian Gourieroux & Joann Jasiak, 2016. "Filtering, Prediction and Simulation Methods for Noncausal Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 405-430, May.
    3. Nelimarkka, Jaakko, 2017. "Evidence on News Shocks under Information Deficiency," MPRA Paper 80850, University Library of Munich, Germany.
    4. Nalan Baştürk & Stefano Grassi & Lennart Hoogerheide & Herman K. Van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM," Econometrics, MDPI, vol. 4(1), pages 1-20, March.
    5. Ülkü, Numan & Kuruppuarachchi, Duminda & Kuzmicheva, Olga, 2017. "Stock market's response to real output shocks in Eastern European frontier markets: A VARwAL model," Emerging Markets Review, Elsevier, vol. 33(C), pages 140-154.
    6. Nelimarkka, Jaakko, 2017. "The effects of government spending under anticipation: the noncausal VAR approach," MPRA Paper 81303, University Library of Munich, Germany.
    7. Gianluca Cubadda & Francesco Giancaterini & Stefano Grassi, 2025. "Sequential Monte Carlo for Noncausal Processes," Papers 2501.03945, arXiv.org.

    More about this item

    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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