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Does noncausality help in forecasting economic time series?

Author

Listed:
  • Henri Nyberg

    () (University of Helsinki)

  • Markku Lanne

    () (University of Helsinki)

  • Erkka Saarinen

    () (University of Helsinki)

Abstract

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models. For a collection of quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater.

Suggested Citation

  • Henri Nyberg & Markku Lanne & Erkka Saarinen, 2012. "Does noncausality help in forecasting economic time series?," Economics Bulletin, AccessEcon, vol. 32(4), pages 2849-2859.
  • Handle: RePEc:ebl:ecbull:eb-12-00360
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2012/Volume32/EB-12-V32-I4-P274.pdf
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    References listed on IDEAS

    as
    1. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2012. "Optimal forecasting of noncausal autoregressive time series," International Journal of Forecasting, Elsevier, vol. 28(3), pages 623-631.
    2. Markku Lanne & Arto Luoma & Jani Luoto, 2012. "Bayesian Model Selection And Forecasting In Noncausal Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 812-830, August.
    3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, pages 499-526.
    4. Lanne, Markku & Nyberg, Henri & Saarinen, Erkka, 2011. "Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison," MPRA Paper 30254, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Giurcanu, Mihai C., 2015. "A simulation algorithm for non-causal VARMA processes," Statistics & Probability Letters, Elsevier, pages 65-72.
    2. Nyberg, Henri & Saikkonen, Pentti, 2014. "Forecasting with a noncausal VAR model," Computational Statistics & Data Analysis, Elsevier, pages 536-555.
    3. Fries, Sébastien & Zakoian, Jean-Michel, 2017. "Mixed Causal-Noncausal AR Processes and the Modelling of Explosive Bubbles," MPRA Paper 81345, University Library of Munich, Germany.
    4. Hecq, Alain & Issler, João Victor & Telg, Sean, 2017. "Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors," MPRA Paper 80767, University Library of Munich, Germany.
    5. repec:gam:jecnmx:v:5:y:2017:i:4:p:48-:d:117025 is not listed on IDEAS
    6. Hecq A.W. & Lieb L.M. & Telg J.M.A., 2015. "Identification of Mixed Causal-Noncausal Models : How Fat Should We Go?," Research Memorandum 035, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Nyberg, Henri & Saikkonen, Pentti, 2014. "Forecasting with a noncausal VAR model," Computational Statistics & Data Analysis, Elsevier, pages 536-555.
    8. Alain Hecq & Sean Telg & Lenard Lieb, 2017. "Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?," Econometrics, MDPI, Open Access Journal, vol. 5(4), pages 1-22, October.
    9. Gouriéroux, Christian & Zakoian, Jean-Michel, 2016. "Local Explosion Modelling by Noncausal Process," MPRA Paper 71105, University Library of Munich, Germany.
    10. Nyholm, Juho, 2017. "Residual-based diagnostic tests for noninvertible ARMA models," MPRA Paper 81033, University Library of Munich, Germany.
    11. repec:eee:eneeco:v:65:y:2017:i:c:p:424-433 is not listed on IDEAS

    More about this item

    Keywords

    Noncausal autoregression; forecast comparison; macroeconomic variables; financial variables;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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